Community evolution mining and analysis in social network

NASA Astrophysics Data System (ADS)

Liu, Hongtao; Tian, Yuan; Liu, Xueyan; Jian, Jie

2017-03-01

With the development of digital and network technology, various social platforms emerge. These social platforms have greatly facilitated access to information, attracting more and more users. They use these social platforms every day to work, study and communicate, so every moment social platforms are generating massive amounts of data. These data can often be modeled as complex networks, making large-scale social network analysis possible. In this paper, the existing evolution classification model of community has been improved based on community evolution relationship over time in dynamic social network, and the Evolution-Tree structure is proposed which can show the whole life cycle of the community more clearly. The comparative test result shows that the improved model can excavate the evolution relationship of the community well.

Rumor Diffusion in an Interests-Based Dynamic Social Network

PubMed Central

Mao, Xinjun; Guessoum, Zahia; Zhou, Huiping

2013-01-01

To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend networks have been conducted. The results show some interesting observations: (1) positive information may evolve to become a rumor through the diffusion process that people may modify the information by word of mouth; (2) with the same average degree, a random social network has a smaller clustering coefficient and is more beneficial for rumor diffusion than the dynamic friend network; (3) a rumor is spread more widely in a social network with a smaller global clustering coefficient than in a social network with a larger global clustering coefficient; and (4) a network with a smaller clustering coefficient has a larger efficiency. PMID:24453911

Rumor diffusion in an interests-based dynamic social network.

PubMed

Tang, Mingsheng; Mao, Xinjun; Guessoum, Zahia; Zhou, Huiping

2013-01-01

To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend networks have been conducted. The results show some interesting observations: (1) positive information may evolve to become a rumor through the diffusion process that people may modify the information by word of mouth; (2) with the same average degree, a random social network has a smaller clustering coefficient and is more beneficial for rumor diffusion than the dynamic friend network; (3) a rumor is spread more widely in a social network with a smaller global clustering coefficient than in a social network with a larger global clustering coefficient; and (4) a network with a smaller clustering coefficient has a larger efficiency.

Trust Maximization in Social Networks

NASA Astrophysics Data System (ADS)

Zhan, Justin; Fang, Xing

Trust is a human-related phenomenon in social networks. Trust research on social networks has gained much attention on its usefulness, and on modeling propagations. There is little focus on finding maximum trust in social networks which is particularly important when a social network is oriented by certain tasks. In this paper, we propose a trust maximization algorithm based on the task-oriented social networks.

Engineering online and in-person social networks to sustain physical activity: application of a conceptual model

PubMed Central

2013-01-01

Background High rates of physical inactivity compromise the health status of populations globally. Social networks have been shown to influence physical activity (PA), but little is known about how best to engineer social networks to sustain PA. To improve procedures for building networks that shape PA as a normative behavior, there is a need for more specific hypotheses about how social variables influence PA. There is also a need to integrate concepts from network science with ecological concepts that often guide the design of in-person and electronically-mediated interventions. Therefore, this paper: (1) proposes a conceptual model that integrates principles from network science and ecology across in-person and electronically-mediated intervention modes; and (2) illustrates the application of this model to the design and evaluation of a social network intervention for PA. Methods/Design A conceptual model for engineering social networks was developed based on a scoping literature review of modifiable social influences on PA. The model guided the design of a cluster randomized controlled trial in which 308 sedentary adults were randomly assigned to three groups: WalkLink+: prompted and provided feedback on participants’ online and in-person social-network interactions to expand networks for PA, plus provided evidence-based online walking program and weekly walking tips; WalkLink: evidence-based online walking program and weekly tips only; Minimal Treatment Control: weekly tips only. The effects of these treatment conditions were assessed at baseline, post-program, and 6-month follow-up. The primary outcome was accelerometer-measured PA. Secondary outcomes included objectively-measured aerobic fitness, body mass index, waist circumference, blood pressure, and neighborhood walkability; and self-reported measures of the physical environment, social network environment, and social network interactions. The differential effects of the three treatment conditions on primary and secondary outcomes will be analyzed using general linear modeling (GLM), or generalized linear modeling if the assumptions for GLM cannot be met. Discussion Results will contribute to greater understanding of how to conceptualize and implement social networks to support long-term PA. Establishing social networks for PA across multiple life settings could contribute to cultural norms that sustain active living. Trial registration ClinicalTrials.gov NCT01142804 PMID:23945138

Graduate Employability: The Perspective of Social Network Learning

ERIC Educational Resources Information Center

Chen, Yong

2017-01-01

This study provides a conceptual framework for understanding how the graduate acquire employability through the social network in the Chinese context, using insights from the social network theory. This paper builds a conceptual model of the relationship among social network, social network learning and the graduate employability, and uses…

Stories in Networks and Networks in Stories: A Tri-Modal Model for Mixed-Methods Social Network Research on Teachers

ERIC Educational Resources Information Center

Baker-Doyle, Kira J.

2015-01-01

Social network research on teachers and schools has risen exponentially in recent years as an innovative method to reveal the role of social networks in education. However, scholars are still exploring ways to incorporate traditional quantitative methods of Social Network Analysis (SNA) with qualitative approaches to social network research. This…

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases.

PubMed

Masías, Víctor Hugo; Valle, Mauricio; Morselli, Carlo; Crespo, Fernando; Vargas, Augusto; Laengle, Sigifredo

2016-01-01

Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers-Logistic Regression, Naïve Bayes and Random Forest-with a range of social network measures and the necessary databases to model the verdicts in two real-world cases: the U.S. Watergate Conspiracy of the 1970's and the now-defunct Canada-based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.

Multiple Factors-Aware Diffusion in Social Networks

DTIC Science & Technology

2015-05-22

Multiple Factors-Aware Diffusion in Social Networks Chung-Kuang Chou(B) and Ming-Syan Chen Department of Electrical Engineering, National Taiwan...propagates from nodes to nodes over a social network . The behavior that a node adopts an information piece in a social network can be affected by...Twitter dataset. Keywords: Social networks · Diffusion models 1 Introduction Information diffusion in social networks has been an active research field

Modeling Epidemics Spreading on Social Contact Networks.

PubMed

Zhang, Zhaoyang; Wang, Honggang; Wang, Chonggang; Fang, Hua

2015-09-01

Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion.

Modeling Epidemics Spreading on Social Contact Networks

PubMed Central

ZHANG, ZHAOYANG; WANG, HONGGANG; WANG, CHONGGANG; FANG, HUA

2016-01-01

Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion. PMID:27722037

Visual social network analysis: effective approach to model complex human social, behaviour & culture.

PubMed

Ahram, Tareq Z; Karwowski, Waldemar

2012-01-01

The advent and adoption of internet-based social networking has significantly altered our daily lives. The educational community has taken notice of the positive aspects of social networking such as creation of blogs and to support groups of system designers going through the same challenges and difficulties. This paper introduces a social networking framework for collaborative education, design and modeling of the next generation of smarter products and services. Human behaviour modeling in social networking application aims to ensure that human considerations for learners and designers have a prominent place in the integrated design and development of sustainable, smarter products throughout the total system lifecycle. Social networks blend self-directed learning and prescribed, existing information. The self-directed element creates interest within a learner and the ability to access existing information facilitates its transfer, and eventual retention of knowledge acquired.

Link-prediction to tackle the boundary specification problem in social network surveys

PubMed Central

De Wilde, Philippe; Buarque de Lima-Neto, Fernando

2017-01-01

Diffusion processes in social networks often cause the emergence of global phenomena from individual behavior within a society. The study of those global phenomena and the simulation of those diffusion processes frequently require a good model of the global network. However, survey data and data from online sources are often restricted to single social groups or features, such as age groups, single schools, companies, or interest groups. Hence, a modeling approach is required that extrapolates the locally restricted data to a global network model. We tackle this Missing Data Problem using Link-Prediction techniques from social network research, network generation techniques from the area of Social Simulation, as well as a combination of both. We found that techniques employing less information may be more adequate to solve this problem, especially when data granularity is an issue. We validated the network models created with our techniques on a number of real-world networks, investigating degree distributions as well as the likelihood of links given the geographical distance between two nodes. PMID:28426826

Social power and opinion formation in complex networks

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2013-02-01

In this paper we investigate the effects of social power on the evolution of opinions in model networks as well as in a number of real social networks. A continuous opinion formation model is considered and the analysis is performed through numerical simulation. Social power is given to a proportion of agents selected either randomly or based on their degrees. As artificial network structures, we consider scale-free networks constructed through preferential attachment and Watts-Strogatz networks. Numerical simulations show that scale-free networks with degree-based social power on the hub nodes have an optimal case where the largest number of the nodes reaches a consensus. However, given power to a random selection of nodes could not improve consensus properties. Introducing social power in Watts-Strogatz networks could not significantly change the consensus profile.

Examining the Relationships Between Education, Social Networks and Democratic Support With ABM

NASA Technical Reports Server (NTRS)

Drucker, Nick; Campbell, Kenyth

2011-01-01

This paper introduces an agent-based model that explores the relationships between education, social networks, and support for democratic ideals. This study examines two factors thai affect democratic support, education, and social networks. Current theory concerning these two variables suggests that positive relationships exist between education and democratic support and between social networks and the spread of ideas. The model contains multiple variables of democratic support, two of which are evaluated through experimentation. The model allows individual entities within the system to make "decisions" about their democratic support independent of one another. The agent based approach also allows entities to utilize their social networks to spread ideas. Current theory supports experimentation results. In addion , these results show the model is capable of reproducing real world outcomes. This paper addresses the model creation process and the experimentation procedure, as well as future research avenues and potential shortcomings of the model

Modeling the dynamics of evaluation: a multilevel neural network implementation of the iterative reprocessing model.

PubMed

Ehret, Phillip J; Monroe, Brian M; Read, Stephen J

2015-05-01

We present a neural network implementation of central components of the iterative reprocessing (IR) model. The IR model argues that the evaluation of social stimuli (attitudes, stereotypes) is the result of the IR of stimuli in a hierarchy of neural systems: The evaluation of social stimuli develops and changes over processing. The network has a multilevel, bidirectional feedback evaluation system that integrates initial perceptual processing and later developing semantic processing. The network processes stimuli (e.g., an individual's appearance) over repeated iterations, with increasingly higher levels of semantic processing over time. As a result, the network's evaluations of stimuli evolve. We discuss the implications of the network for a number of different issues involved in attitudes and social evaluation. The success of the network supports the IR model framework and provides new insights into attitude theory. © 2014 by the Society for Personality and Social Psychology, Inc.

Upstream and downstream correlates of older people's engagement in social networks: what are their effects on health over time?

PubMed

Stephens, Christine; Noone, Jack; Alpass, Fiona

2014-01-01

This study tested the effects of social network engagement and social support on the health of older people moving into retirement, using a model which includes social context variables. A prospective survey of a New Zealand population sample aged 54-70 at baseline (N = 2,282) was used to assess the effects on mental and physical health across time. A structural equation model assessed pathways from the social context variables through network engagement to social support and then to mental and physical health 2 years later. The proposed model of effects on mental health was supported when gender, economic living standards, and ethnicity were included along with the direct effects of these variables on social support. These findings confirm the importance of taking social context variables into account when considering social support networks. Social engagement appears to be an important aspect of social network functioning which could be investigated further.

A growing social network model in geographical space

NASA Astrophysics Data System (ADS)

Antonioni, Alberto; Tomassini, Marco

2017-09-01

In this work we propose a new model for the generation of social networks that includes their often ignored spatial aspects. The model is a growing one and links are created either taking space into account, or disregarding space and only considering the degree of target nodes. These two effects can be mixed linearly in arbitrary proportions through a parameter. We numerically show that for a given range of the combination parameter, and for given mean degree, the generated network class shares many important statistical features with those observed in actual social networks, including the spatial dependence of connections. Moreover, we show that the model provides a good qualitative fit to some measured social networks.

The association between neighborhood social capital and self-reported dentate status in elderly Japanese--the Ohsaki Cohort 2006 Study.

PubMed

Aida, J; Kuriyama, S; Ohmori-Matsuda, K; Hozawa, A; Osaka, K; Tsuji, I

2011-06-01

Little is known about the influence of social capital on dental health. The aim of the present cross-sectional study was to determine the association between neighborhood social capital, individual social networks and social support and the number of remaining teeth in elderly Japanese. In December 2006, self-administered questionnaires were sent to 31,237 eligible community-dwelling individuals (response rate: 73.9%). Included in the analysis were 21,736 participants. Five neighborhood social capital variables were calculated from individual civic networks, sports and hobby networks, volunteer networks, friendship networks and social support variables. We used multilevel logistic regression models to estimate the odds ratio (OR) of having 20 or more teeth according to neighborhood social capital variables with adjustment for sex, age, individual social networks and social support, educational attainment, neighborhood educational level, dental health behavior, smoking status, history of diabetes and self-rated health. The average age of the participants was 74.9 (standard deviation; 6.6) years, and 28.5% of them had 20 or more teeth. In the univariate multilevel model, there were statistically significant associations between neighborhood sports and hobby networks, friendship networks and self-reported dentate status. In the multivariable multilevel model, compared with participants living in lowest friendship network neighborhoods, those living in highest friendship network neighborhoods had an OR 1.17 (95% confidence interval, 1.04-1.30) times higher for having 20 or more teeth. There is a significant association between one network aspect of neighborhood social capital and individual dentate status regardless of individual social networks and social support. © 2010 John Wiley & Sons A/S.

Predicting Employee Turnover from Communication Networks.

ERIC Educational Resources Information Center

Feeley, Thomas H.; Barnett, George A.

1997-01-01

Investigates three social network models of employee turnover: a structural equivalence model, a social influence model, and an erosion model. Administers a communication network questionnaire to all 170 employees of an organization. Finds support for all three models of turnover, with the erosion model explaining more of the variance than do the…

Aperiodic dynamics in a deterministic adaptive network model of attitude formation in social groups

NASA Astrophysics Data System (ADS)

Ward, Jonathan A.; Grindrod, Peter

2014-07-01

Adaptive network models, in which node states and network topology coevolve, arise naturally in models of social dynamics that incorporate homophily and social influence. Homophily relates the similarity between pairs of nodes' states to their network coupling strength, whilst social influence causes coupled nodes' states to convergence. In this paper we propose a deterministic adaptive network model of attitude formation in social groups that includes these effects, and in which the attitudinal dynamics are represented by an activato-inhibitor process. We illustrate that consensus, corresponding to all nodes adopting the same attitudinal state and being fully connected, may destabilise via Turing instability, giving rise to aperiodic dynamics with sensitive dependence on initial conditions. These aperiodic dynamics correspond to the formation and dissolution of sub-groups that adopt contrasting attitudes. We discuss our findings in the context of cultural polarisation phenomena. Social influence. This reflects the fact that people tend to modify their behaviour and attitudes in response to the opinions of others [22-26]. We model social influence via diffusion: agents adjust their state according to a weighted sum (dictated by the evolving network) of the differences between their state and the states of their neighbours. Homophily. This relates the similarity of individuals' states to their frequency and strength of interaction [27]. Thus in our model, homophily drives the evolution of the weighted ‘social' network. A precise formulation of our model is given in Section 2. Social influence and homophily underpin models of social dynamics [21], which cover a wide range of sociological phenomena, including the diffusion of innovations [28-32], complex contagions [33-36], collective action [37-39], opinion dynamics [19,20,40,10,11,13,15,41,16], the emergence of social norms [42-44], group stability [45], social differentiation [46] and, of particular relevance here, cultural dissemination [47,12,48].Combining the effects of social influence and homophily naturally gives rise to an adaptive network, since social influence causes the states of agents that are strongly connected to become more similar, while homophily strengthens connections between agents whose states are already similar.1

Mental Health, School Problems, and Social Networks: Modeling Urban Adolescent Substance Use

ERIC Educational Resources Information Center

Mason, Michael J.

2010-01-01

This study tested a mediation model of the relationship with school problems, social network quality, and substance use with a primary care sample of 301 urban adolescents. It was theorized that social network quality (level of risk or protection in network) would mediate the effects of school problems, accounting for internalizing problems and…

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases

PubMed Central

2016-01-01

Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers–Logistic Regression, Naïve Bayes and Random Forest–with a range of social network measures and the necessary databases to model the verdicts in two real–world cases: the U.S. Watergate Conspiracy of the 1970’s and the now–defunct Canada–based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures. PMID:26824351

Model of community emergence in weighted social networks

NASA Astrophysics Data System (ADS)

Kumpula, J. M.; Onnela, J.-P.; Saramäki, J.; Kertész, J.; Kaski, K.

2009-04-01

Over the years network theory has proven to be rapidly expanding methodology to investigate various complex systems and it has turned out to give quite unparalleled insight to their structure, function, and response through data analysis, modeling, and simulation. For social systems in particular the network approach has empirically revealed a modular structure due to interplay between the network topology and link weights between network nodes or individuals. This inspired us to develop a simple network model that could catch some salient features of mesoscopic community and macroscopic topology formation during network evolution. Our model is based on two fundamental mechanisms of network sociology for individuals to find new friends, namely cyclic closure and focal closure, which are mimicked by local search-link-reinforcement and random global attachment mechanisms, respectively. In addition we included to the model a node deletion mechanism by removing all its links simultaneously, which corresponds for an individual to depart from the network. Here we describe in detail the implementation of our model algorithm, which was found to be computationally efficient and produce many empirically observed features of large-scale social networks. Thus this model opens a new perspective for studying such collective social phenomena as spreading, structure formation, and evolutionary processes.

Epidemics in adaptive networks with community structure

NASA Astrophysics Data System (ADS)

Shaw, Leah; Tunc, Ilker

2010-03-01

Models for epidemic spread on static social networks do not account for changes in individuals' social interactions. Recent studies of adaptive networks have modeled avoidance behavior, as non-infected individuals try to avoid contact with infectives. Such models have not generally included realistic social structure. Here we study epidemic spread on an adaptive network with community structure. We model the effect of heterogeneous communities on infection levels and epidemic extinction. We also show how an epidemic can alter the community structure.

Spreading in online social networks: the role of social reinforcement.

PubMed

Zheng, Muhua; Lü, Linyuan; Zhao, Ming

2013-07-01

Some epidemic spreading models are usually applied to analyze the propagation of opinions or news. However, the dynamics of epidemic spreading and information or behavior spreading are essentially different in many aspects. Centola's experiments [Science 329, 1194 (2010)] on behavior spreading in online social networks showed that the spreading is faster and broader in regular networks than in random networks. This result contradicts with the former understanding that random networks are preferable for spreading than regular networks. To describe the spreading in online social networks, a unknown-known-approved-exhausted four-status model was proposed, which emphasizes the effect of social reinforcement and assumes that the redundant signals can improve the probability of approval (i.e., the spreading rate). Performing the model on regular and random networks, it is found that our model can well explain the results of Centola's experiments on behavior spreading and some former studies on information spreading in different parameter space. The effects of average degree and network size on behavior spreading process are further analyzed. The results again show the importance of social reinforcement and are accordant with Centola's anticipation that increasing the network size or decreasing the average degree will enlarge the difference of the density of final approved nodes between regular and random networks. Our work complements the former studies on spreading dynamics, especially the spreading in online social networks where the information usually requires individuals' confirmations before being transmitted to others.

Social network models predict movement and connectivity in ecological landscapes

USGS Publications Warehouse

Fletcher, R.J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, W.M.

2011-01-01

Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

Stylized facts in social networks: Community-based static modeling

NASA Astrophysics Data System (ADS)

Jo, Hang-Hyun; Murase, Yohsuke; Török, János; Kertész, János; Kaski, Kimmo

2018-06-01

The past analyses of datasets of social networks have enabled us to make empirical findings of a number of aspects of human society, which are commonly featured as stylized facts of social networks, such as broad distributions of network quantities, existence of communities, assortative mixing, and intensity-topology correlations. Since the understanding of the structure of these complex social networks is far from complete, for deeper insight into human society more comprehensive datasets and modeling of the stylized facts are needed. Although the existing dynamical and static models can generate some stylized facts, here we take an alternative approach by devising a community-based static model with heterogeneous community sizes and larger communities having smaller link density and weight. With these few assumptions we are able to generate realistic social networks that show most stylized facts for a wide range of parameters, as demonstrated numerically and analytically. Since our community-based static model is simple to implement and easily scalable, it can be used as a reference system, benchmark, or testbed for further applications.

Control Theoretic Modeling for Uncertain Cultural Attitudes and Unknown Adversarial Intent

DTIC Science & Technology

2009-02-01

Constructive computational tools. 15. SUBJECT TERMS social learning, social networks , multiagent systems, game theory 16. SECURITY CLASSIFICATION OF: a...over- reactionary behaviors; 3) analysis of rational social learning in networks : analysis of belief propagation in social networks in various...general methodology as a predictive device for social network formation and for communication network formation with constraints on the lengths of

Social network types among older Korean adults: Associations with subjective health.

PubMed

Sohn, Sung Yun; Joo, Won-Tak; Kim, Woo Jung; Kim, Se Joo; Youm, Yoosik; Kim, Hyeon Chang; Park, Yeong-Ran; Lee, Eun

2017-01-01

With population aging now a global phenomenon, the health of older adults is becoming an increasingly important issue. Because the Korean population is aging at an unprecedented rate, preparing for public health problems associated with old age is particularly salient in this country. As the physical and mental health of older adults is related to their social relationships, investigating the social networks of older adults and their relationship to health status is important for establishing public health policies. The aims of this study were to identify social network types among older adults in South Korea and to examine the relationship of these social network types with self-rated health and depression. Data from the Korean Social Life, Health, and Aging Project were analyzed. Model-based clustering using finite normal mixture modeling was conducted to identify the social network types based on ten criterion variables of social relationships and activities: marital status, number of children, number of close relatives, number of friends, frequency of attendance at religious services, attendance at organized group meetings, in-degree centrality, out-degree centrality, closeness centrality, and betweenness centrality. Multivariate regression analysis was conducted to examine associations between the identified social network types and self-rated health and depression. The model-based clustering analysis revealed that social networks clustered into five types: diverse, family, congregant, congregant-restricted, and restricted. Diverse or family social network types were significantly associated with more favorable subjective mental health, whereas the restricted network type was significantly associated with poorer ratings of mental and physical health. In addition, our analysis identified unique social network types related to religious activities. In summary, we developed a comprehensive social network typology for older Korean adults. Copyright © 2016 Elsevier Ltd. All rights reserved.

An economic model of friendship and enmity for measuring social balance in networks

NASA Astrophysics Data System (ADS)

Lee, Kyu-Min; Shin, Euncheol; You, Seungil

2017-12-01

We propose a dynamic economic model of networks where agents can be friends or enemies with one another. This is a decentralized relationship model in that agents decide whether to change their relationships so as to minimize their imbalanced triads. In this model, there is a single parameter, which we call social temperature, that captures the degree to which agents care about social balance in their relationships. We show that the global structure of relationship configuration converges to a unique stationary distribution. Using this stationary distribution, we characterize the maximum likelihood estimator of the social temperature parameter. Since the estimator is computationally challenging to calculate from real social network datasets, we provide a simple simulation algorithm and verify its performance with real social network datasets.

Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach

PubMed Central

Senior, Alistair M.; Lihoreau, Mathieu; Buhl, Jerome; Raubenheimer, David; Simpson, Stephen J.

2016-01-01

Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments. PMID:26858671

Polarity related influence maximization in signed social networks.

PubMed

Li, Dong; Xu, Zhi-Ming; Chakraborty, Nilanjan; Gupta, Anika; Sycara, Katia; Li, Sheng

2014-01-01

Influence maximization in social networks has been widely studied motivated by applications like spread of ideas or innovations in a network and viral marketing of products. Current studies focus almost exclusively on unsigned social networks containing only positive relationships (e.g. friend or trust) between users. Influence maximization in signed social networks containing both positive relationships and negative relationships (e.g. foe or distrust) between users is still a challenging problem that has not been studied. Thus, in this paper, we propose the polarity-related influence maximization (PRIM) problem which aims to find the seed node set with maximum positive influence or maximum negative influence in signed social networks. To address the PRIM problem, we first extend the standard Independent Cascade (IC) model to the signed social networks and propose a Polarity-related Independent Cascade (named IC-P) diffusion model. We prove that the influence function of the PRIM problem under the IC-P model is monotonic and submodular Thus, a greedy algorithm can be used to achieve an approximation ratio of 1-1/e for solving the PRIM problem in signed social networks. Experimental results on two signed social network datasets, Epinions and Slashdot, validate that our approximation algorithm for solving the PRIM problem outperforms state-of-the-art methods.

Polarity Related Influence Maximization in Signed Social Networks

PubMed Central

Li, Dong; Xu, Zhi-Ming; Chakraborty, Nilanjan; Gupta, Anika; Sycara, Katia; Li, Sheng

2014-01-01

Influence maximization in social networks has been widely studied motivated by applications like spread of ideas or innovations in a network and viral marketing of products. Current studies focus almost exclusively on unsigned social networks containing only positive relationships (e.g. friend or trust) between users. Influence maximization in signed social networks containing both positive relationships and negative relationships (e.g. foe or distrust) between users is still a challenging problem that has not been studied. Thus, in this paper, we propose the polarity-related influence maximization (PRIM) problem which aims to find the seed node set with maximum positive influence or maximum negative influence in signed social networks. To address the PRIM problem, we first extend the standard Independent Cascade (IC) model to the signed social networks and propose a Polarity-related Independent Cascade (named IC-P) diffusion model. We prove that the influence function of the PRIM problem under the IC-P model is monotonic and submodular Thus, a greedy algorithm can be used to achieve an approximation ratio of 1-1/e for solving the PRIM problem in signed social networks. Experimental results on two signed social network datasets, Epinions and Slashdot, validate that our approximation algorithm for solving the PRIM problem outperforms state-of-the-art methods. PMID:25061986

Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction

PubMed Central

Escalera, Sergio; Baró, Xavier; Vitrià, Jordi; Radeva, Petia; Raducanu, Bogdan

2012-01-01

Social interactions are a very important component in people’s lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Times’ Blogging Heads opinion blog. The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The links’ weights are a measure of the “influence” a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network. PMID:22438733

From sparse to dense and from assortative to disassortative in online social networks

PubMed Central

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-01-01

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks. PMID:24798703

From sparse to dense and from assortative to disassortative in online social networks.

PubMed

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-05-06

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.

Towards a Social Networks Model for Online Learning & Performance

ERIC Educational Resources Information Center

Chung, Kon Shing Kenneth; Paredes, Walter Christian

2015-01-01

In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…

An information spreading model based on online social networks

NASA Astrophysics Data System (ADS)

Wang, Tao; He, Juanjuan; Wang, Xiaoxia

2018-01-01

Online social platforms are very popular in recent years. In addition to spreading information, users could review or collect information on online social platforms. According to the information spreading rules of online social network, a new information spreading model, namely IRCSS model, is proposed in this paper. It includes sharing mechanism, reviewing mechanism, collecting mechanism and stifling mechanism. Mean-field equations are derived to describe the dynamics of the IRCSS model. Moreover, the steady states of reviewers, collectors and stiflers and the effects of parameters on the peak values of reviewers, collectors and sharers are analyzed. Finally, numerical simulations are performed on different networks. Results show that collecting mechanism and reviewing mechanism, as well as the connectivity of the network, make information travel wider and faster, and compared to WS network and ER network, the speed of reviewing, sharing and collecting information is fastest on BA network.

An agent-based model of centralized institutions, social network technology, and revolution.

PubMed

Makowsky, Michael D; Rubin, Jared

2013-01-01

This paper sheds light on the general mechanisms underlying large-scale social and institutional change. We employ an agent-based model to test the impact of authority centralization and social network technology on preference falsification and institutional change. We find that preference falsification is increasing with centralization and decreasing with social network range. This leads to greater cascades of preference revelation and thus more institutional change in highly centralized societies and this effect is exacerbated at greater social network ranges. An empirical analysis confirms the connections that we find between institutional centralization, social radius, preference falsification, and institutional change.

Modelling the public opinion transmission on social networks under opinion leaders

NASA Astrophysics Data System (ADS)

Li, Zuozhi; Li, Meng; Ji, Wanwan

2017-06-01

In this paper, based on Social Network Analysis (SNA), the social network model of opinion leaders influencing the public opinion transmission is explored. The hot event, A Female Driver Was Beaten Due To Lane Change, has characteristics of individual short-term and non-government intervention, which is used to data extraction, and formed of the network structure on opinion leaders influencing the public opinion transmission. And the evolution mechanism are analyzed in the three evolutionary situations. Opinion leaders influence micro-blogging public opinion on social network evolution model shows that this type of network public opinion transmission is largely constrained by opinion leaders, so the opinion leaders behavior supervising on the spread of this public opinion is pivotal, and which has a guiding significance.

Enhancing social networks: a qualitative study of health and social care practice in UK mental health services.

PubMed

Webber, Martin; Reidy, Hannah; Ansari, David; Stevens, Martin; Morris, David

2015-03-01

People with severe mental health problems such as psychosis have access to less social capital, defined as resources within social networks, than members of the general population. However, a lack of theoretically and empirically informed models hampers the development of social interventions which seek to enhance an individual's social networks. This paper reports the findings of a qualitative study, which used ethnographic field methods in six sites in England to investigate how workers helped people recovering from psychosis to enhance their social networks. This study drew upon practice wisdom and lived experience to provide data for intervention modelling. Data were collected from 73 practitioners and 51 people who used their services in two phases. Data were selected and coded using a grounded theory approach to depict the key themes that appeared to underpin the generation of social capital within networks. Findings are presented in four over-arching themes - worker skills, attitudes and roles; connecting people processes; role of the agency; and barriers to network development. The sub-themes which were identified included worker attitudes; person-centred approach; equality of worker-individual relationship; goal setting; creating new networks and relationships; engagement through activities; practical support; existing relationships; the individual taking responsibility; identifying and overcoming barriers; and moving on. Themes were consistent with recovery models used within mental health services and will provide the basis for the development of an intervention model to enhance individuals' access to social capital within networks. © 2014 John Wiley & Sons Ltd.

Spatial Epidemic Modelling in Social Networks

NASA Astrophysics Data System (ADS)

Simoes, Joana Margarida

2005-06-01

The spread of infectious diseases is highly influenced by the structure of the underlying social network. The target of this study is not the network of acquaintances, but the social mobility network: the daily movement of people between locations, in regions. It was already shown that this kind of network exhibits small world characteristics. The model developed is agent based (ABM) and comprehends a movement model and a infection model. In the movement model, some assumptions are made about its structure and the daily movement is decomposed into four types: neighborhood, intra region, inter region and random. The model is Geographical Information Systems (GIS) based, and uses real data to define its geometry. Because it is a vector model, some optimization techniques were used to increase its efficiency.

From social network (centralized vs. decentralized) to collective decision-making (unshared vs. shared consensus).

PubMed

Sueur, Cédric; Deneubourg, Jean-Louis; Petit, Odile

2012-01-01

Relationships we have with our friends, family, or colleagues influence our personal decisions, as well as decisions we make together with others. As in human beings, despotism and egalitarian societies seem to also exist in animals. While studies have shown that social networks constrain many phenomena from amoebae to primates, we still do not know how consensus emerges from the properties of social networks in many biological systems. We created artificial social networks that represent the continuum from centralized to decentralized organization and used an agent-based model to make predictions about the patterns of consensus and collective movements we observed according to the social network. These theoretical results showed that different social networks and especially contrasted ones--star network vs. equal network--led to totally different patterns. Our model showed that, by moving from a centralized network to a decentralized one, the central individual seemed to lose its leadership in the collective movement's decisions. We, therefore, showed a link between the type of social network and the resulting consensus. By comparing our theoretical data with data on five groups of primates, we confirmed that this relationship between social network and consensus also appears to exist in animal societies.

Asymptotic theory of time-varying social networks with heterogeneous activity and tie allocation.

PubMed

Ubaldi, Enrico; Perra, Nicola; Karsai, Márton; Vezzani, Alessandro; Burioni, Raffaella; Vespignani, Alessandro

2016-10-24

The dynamic of social networks is driven by the interplay between diverse mechanisms that still challenge our theoretical and modelling efforts. Amongst them, two are known to play a central role in shaping the networks evolution, namely the heterogeneous propensity of individuals to i) be socially active and ii) establish a new social relationships with their alters. Here, we empirically characterise these two mechanisms in seven real networks describing temporal human interactions in three different settings: scientific collaborations, Twitter mentions, and mobile phone calls. We find that the individuals' social activity and their strategy in choosing ties where to allocate their social interactions can be quantitatively described and encoded in a simple stochastic network modelling framework. The Master Equation of the model can be solved in the asymptotic limit. The analytical solutions provide an explicit description of both the system dynamic and the dynamical scaling laws characterising crucial aspects about the evolution of the networks. The analytical predictions match with accuracy the empirical observations, thus validating the theoretical approach. Our results provide a rigorous dynamical system framework that can be extended to include other processes shaping social dynamics and to generate data driven predictions for the asymptotic behaviour of social networks.

Asymptotic theory of time-varying social networks with heterogeneous activity and tie allocation

NASA Astrophysics Data System (ADS)

Ubaldi, Enrico; Perra, Nicola; Karsai, Márton; Vezzani, Alessandro; Burioni, Raffaella; Vespignani, Alessandro

2016-10-01

The dynamic of social networks is driven by the interplay between diverse mechanisms that still challenge our theoretical and modelling efforts. Amongst them, two are known to play a central role in shaping the networks evolution, namely the heterogeneous propensity of individuals to i) be socially active and ii) establish a new social relationships with their alters. Here, we empirically characterise these two mechanisms in seven real networks describing temporal human interactions in three different settings: scientific collaborations, Twitter mentions, and mobile phone calls. We find that the individuals’ social activity and their strategy in choosing ties where to allocate their social interactions can be quantitatively described and encoded in a simple stochastic network modelling framework. The Master Equation of the model can be solved in the asymptotic limit. The analytical solutions provide an explicit description of both the system dynamic and the dynamical scaling laws characterising crucial aspects about the evolution of the networks. The analytical predictions match with accuracy the empirical observations, thus validating the theoretical approach. Our results provide a rigorous dynamical system framework that can be extended to include other processes shaping social dynamics and to generate data driven predictions for the asymptotic behaviour of social networks.

Veterans with recent substance use and aggression: PTSD, substance use, and social network behaviors.

PubMed

Sexton, Minden B; Davis, Alan K; Buchholz, Katherine R; Winters, Jamie J; Rauch, Sheila A M; Yzquibell, Maegan; Bonar, Erin E; Friday, Steven; Chermack, Stephen T

2018-04-23

Violence is a salient concern among veterans, yet relationships between psychiatric comorbidity, social networks, and aggression are poorly understood. We examined associations between biopsychosocial factors (substance use, posttraumatic stress disorder [PTSD], and social network behaviors) with aggression. We recruited veterans endorsing past-year aggression and substance use (N = 180) from Department of Veterans Affairs outpatient treatment clinics. Main and interaction effects between probable PTSD, substance use, social network violence and substance use, and veteran violence were examined with negative binomial regressions-specifically, physical aggression toward a relationship partner (PA-P), physical injury of a partner (PI-P), physical aggression toward nonpartners (PA-NP), and physical injury of nonpartners (PI-NP). Alcohol use yielded consistent main effects. PTSD and social network violence demonstrated main effects for PA-NP and PI-NP. PTSD and social network violence interacted to predict PA-P such that social network violence appeared salient only in the context of PTSD. PTSD was associated with PI-P, PA-NP, and PI-NP in social network substance use models. In the PA-P model including social network substance use, veterans with PTSD reported greater PA-P in the context of greater social network substance use, whereas veterans without PTSD endorsed PA-P concurrent with greater alcohol frequency. For PI-P, PTSD interacted with alcohol to predict a greater likelihood of partner injury in the context of social network substance use. Investigated variables demonstrated unique associations within the context of specific relationships and the severity of behaviors. Overall, the findings underscore the importance of biopsychosocial models for understanding veteran violence. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Detection of social group instability among captive rhesus macaques using joint network modeling

PubMed Central

Beisner, Brianne A.; Jin, Jian; Fushing, Hsieh; Mccowan, Brenda

2015-01-01

Social stability in group-living animals is an emergent property which arises from the interaction amongst multiple behavioral networks. However, pinpointing when a social group is at risk of collapse is difficult. We used a joint network modeling approach to examine the interdependencies between two behavioral networks, aggression and status signaling, from four stable and three unstable groups of rhesus macaques in order to identify characteristic patterns of network interdependence in stable groups that are readily distinguishable from unstable groups. Our results showed that the most prominent source of aggression-status network interdependence in stable social groups came from more frequent dyads than expected with opposite direction status-aggression (i.e. A threatens B and B signals acceptance of subordinate status). In contrast, unstable groups showed a decrease in opposite direction aggression-status dyads (but remained higher than expected) as well as more frequent than expected dyads with bidirectional aggression. These results demonstrate that not only was the stable joint relationship between aggression and status networks readily distinguishable from unstable time points, social instability manifested in at least two different ways. In sum, our joint modeling approach may prove useful in quantifying and monitoring the complex social dynamics of any wild or captive social system, as all social systems are composed of multiple interconnected networks PMID:26052339

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

PubMed Central

Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D.

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in “real-time”) and forecasting (predicting the future) ILI dynamics in the 2011 – 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets. PMID:29244814

Forecasting influenza-like illness dynamics for military populations using neural networks and social media.

PubMed

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in "real-time") and forecasting (predicting the future) ILI dynamics in the 2011 - 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets.

Emergence of clustering in an acquaintance model without homophily

NASA Astrophysics Data System (ADS)

Bhat, Uttam; Krapivsky, P. L.; Redner, S.

2014-11-01

We introduce an agent-based acquaintance model in which social links are created by processes in which there is no explicit homophily. In spite of the homogeneous nature of the social interactions, highly-clustered social networks can arise. The crucial feature of our model is that of variable transitive interactions. Namely, when an agent introduces two unconnected friends, the rate at which a connection actually occurs between them depends on the number of their mutual acquaintances. As this transitive interaction rate is varied, the social network undergoes a dramatic clustering transition. Close to the transition, the network consists of a collection of well-defined communities. As a function of time, the network can also undergo an incomplete gelation transition, in which the gel, or giant cluster, does not constitute the entire network, even at infinite time. Some of the clustering properties of our model also arise, but in a more gradual manner, in Facebook networks. Finally, we discuss a more realistic variant of our original model in which network realizations can be constructed that quantitatively match Facebook networks.

An Agent-Based Model of Centralized Institutions, Social Network Technology, and Revolution

PubMed Central

Makowsky, Michael D.; Rubin, Jared

2013-01-01

This paper sheds light on the general mechanisms underlying large-scale social and institutional change. We employ an agent-based model to test the impact of authority centralization and social network technology on preference falsification and institutional change. We find that preference falsification is increasing with centralization and decreasing with social network range. This leads to greater cascades of preference revelation and thus more institutional change in highly centralized societies and this effect is exacerbated at greater social network ranges. An empirical analysis confirms the connections that we find between institutional centralization, social radius, preference falsification, and institutional change. PMID:24278280

Measures of node centrality in mobile social networks

NASA Astrophysics Data System (ADS)

Gao, Zhenxiang; Shi, Yan; Chen, Shanzhi

2015-02-01

Mobile social networks exploit human mobility and consequent device-to-device contact to opportunistically create data paths over time. While links in mobile social networks are time-varied and strongly impacted by human mobility, discovering influential nodes is one of the important issues for efficient information propagation in mobile social networks. Although traditional centrality definitions give metrics to identify the nodes with central positions in static binary networks, they cannot effectively identify the influential nodes for information propagation in mobile social networks. In this paper, we address the problems of discovering the influential nodes in mobile social networks. We first use the temporal evolution graph model which can more accurately capture the topology dynamics of the mobile social network over time. Based on the model, we explore human social relations and mobility patterns to redefine three common centrality metrics: degree centrality, closeness centrality and betweenness centrality. We then employ empirical traces to evaluate the benefits of the proposed centrality metrics, and discuss the predictability of nodes' global centrality ranking by nodes' local centrality ranking. Results demonstrate the efficiency of the proposed centrality metrics.

Modeling Social Capital as Dynamic Networks to Promote Access to Oral Healthcare

PubMed Central

Northridge, Mary E.; Kunzel, Carol; Zhang, Qiuyi; Kum, Susan S.; Gilbert, Jessica L.; Jin, Zhu; Metcalf, Sara S.

2016-01-01

Social capital, as comprised of human connections in social networks and their associated benefits, is closely related to the health of individuals, communities, and societies at large. For disadvantaged population groups such as older adults and racial/ethnic minorities, social capital may play a particularly critical role in mitigating the negative effects and reinforcing the positive effects on health. In this project, we model social capital as both cause and effect by simulating dynamic networks. Informed in part by a community-based health promotion program, an agent-based model is contextualized in a GIS environment to explore the complexity of social disparities in oral and general health as experienced at the individual, interpersonal, and community scales. This study provides the foundation for future work investigating how health and healthcare accessibility may be influenced by social networks. PMID:27668298

Modeling Social Capital as Dynamic Networks to Promote Access to Oral Healthcare.

PubMed

Wang, Hua; Northridge, Mary E; Kunzel, Carol; Zhang, Qiuyi; Kum, Susan S; Gilbert, Jessica L; Jin, Zhu; Metcalf, Sara S

2016-01-01

Social capital, as comprised of human connections in social networks and their associated benefits, is closely related to the health of individuals, communities, and societies at large. For disadvantaged population groups such as older adults and racial/ethnic minorities, social capital may play a particularly critical role in mitigating the negative effects and reinforcing the positive effects on health. In this project, we model social capital as both cause and effect by simulating dynamic networks. Informed in part by a community-based health promotion program, an agent-based model is contextualized in a GIS environment to explore the complexity of social disparities in oral and general health as experienced at the individual, interpersonal, and community scales. This study provides the foundation for future work investigating how health and healthcare accessibility may be influenced by social networks.

The Influence of Social Networks and Supports on Depression Symptoms: Differential Pathways for Older Korean Immigrants and Non-Hispanic White Americans.

PubMed

Jeon, Haesang; Lubben, James

The current cross-cultural study examines the pathways underlying different formations of social networks and social support systems, which affect depression symptoms among older Korean immigrants and non-Hispanic Whites in the United States. Data for this study came from a panel survey of 223 older Korean American immigrants and 201 non-Hispanic White older adults 65 years of age and older living in Los Angeles. Structural equation modeling (SEM) is used to test the proposed conceptual model designed to explain the direct and indirect relationships between social networks and social support on depression symptoms. Empirical evidence from this study indicated different effect of one's social networks and social support on depression by race/ethnicity. The work discussed in this article pointed to the need to recognize the role of culture in assessing the relationships between social networks, social support, and health among older adults.

Self-Concealment, Social Network Sites Usage, Social Appearance Anxiety, Loneliness of High School Students: A Model Testing

ERIC Educational Resources Information Center

Dogan, Ugur; Çolak, Tugba Seda

2016-01-01

This study was tested a model for explain to social networks sites (SNS) usage with structural equation modeling (SEM). Using SEM on a sample of 475 high school students (35% male, 65% female) students, model was investigated the relationship between self-concealment, social appearance anxiety, loneliness on SNS such as Twitter and Facebook usage.…

Bridging Social Circles: Need for Cognition, Prejudicial Judgments, and Personal Social Network Characteristics

PubMed Central

Curşeu, Petru L.; de Jong, Jeroen P.

2017-01-01

Various factors pertaining to the social context (availability of plausible social contacts) as well as personality traits influence the emergence of social ties that ultimately compose one’s personal social network. We build on a situational selection model to argue that personality traits influence the cognitive processing of social cues that in turn influences the preference for particular social ties. More specifically, we use a cross-lagged design to test a mediation model explaining the effects of need for cognition (NFC) on egocentric network characteristics. We used the data available in the LISS panel, in which a probabilistic sample of Dutch participants were asked to fill in surveys annually. We tested our model on data collected in three successive years and our results show that people scoring high in NFC tend to revolve in information-rich egocentric networks, characterized by high demographic diversity, high interpersonal dissimilarity, and high average education. The results also show that the effect of NFC on social network characteristics is mediated by non-prejudicial judgments. PMID:28790948

Weighted social networks for a large scale artificial society

NASA Astrophysics Data System (ADS)

Fan, Zong Chen; Duan, Wei; Zhang, Peng; Qiu, Xiao Gang

2016-12-01

The method of artificial society has provided a powerful way to study and explain how individual behaviors at micro level give rise to the emergence of global social phenomenon. It also creates the need for an appropriate representation of social structure which usually has a significant influence on human behaviors. It has been widely acknowledged that social networks are the main paradigm to describe social structure and reflect social relationships within a population. To generate social networks for a population of interest, considering physical distance and social distance among people, we propose a generation model of social networks for a large-scale artificial society based on human choice behavior theory under the principle of random utility maximization. As a premise, we first build an artificial society through constructing a synthetic population with a series of attributes in line with the statistical (census) data for Beijing. Then the generation model is applied to assign social relationships to each individual in the synthetic population. Compared with previous empirical findings, the results show that our model can reproduce the general characteristics of social networks, such as high clustering coefficient, significant community structure and small-world property. Our model can also be extended to a larger social micro-simulation as an input initial. It will facilitate to research and predict some social phenomenon or issues, for example, epidemic transition and rumor spreading.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

DOE PAGES

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; ...

2017-12-15

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

From Social Network (Centralized vs. Decentralized) to Collective Decision-Making (Unshared vs. Shared Consensus)

PubMed Central

Sueur, Cédric; Deneubourg, Jean-Louis; Petit, Odile

2012-01-01

Relationships we have with our friends, family, or colleagues influence our personal decisions, as well as decisions we make together with others. As in human beings, despotism and egalitarian societies seem to also exist in animals. While studies have shown that social networks constrain many phenomena from amoebae to primates, we still do not know how consensus emerges from the properties of social networks in many biological systems. We created artificial social networks that represent the continuum from centralized to decentralized organization and used an agent-based model to make predictions about the patterns of consensus and collective movements we observed according to the social network. These theoretical results showed that different social networks and especially contrasted ones – star network vs. equal network - led to totally different patterns. Our model showed that, by moving from a centralized network to a decentralized one, the central individual seemed to lose its leadership in the collective movement's decisions. We, therefore, showed a link between the type of social network and the resulting consensus. By comparing our theoretical data with data on five groups of primates, we confirmed that this relationship between social network and consensus also appears to exist in animal societies. PMID:22393416

Quantifying Users' Interconnectedness in Online Social Networks - An Indispensible Step for Economic Valuation

NASA Astrophysics Data System (ADS)

Gneiser, Martin; Heidemann, Julia; Klier, Mathias; Landherr, Andrea; Probst, Florian

Online social networks have been gaining increasing economic importance in light of the rising number of their users. Numerous recent acquisitions priced at enormous amounts have illustrated this development and revealed the need for adequate business valuation models. The value of an online social network is largely determined by the value of its users, the relationships between these users, and the resulting network effects. Therefore, the interconnectedness of a user within the network has to be considered explicitly to get a reasonable estimate for the economic value. Established standard business valuation models, however, do not sufficiently take these aspects into account. Thus, we propose a measure based on the PageRank-algorithm to quantify users’ interconnectedness in an online social network. This is a first but indispensible step towards an adequate economic valuation of online social networks.

Communication competence, social support, and depression among college students: a model of facebook and face-to-face support network influence.

PubMed

Wright, Kevin B; Rosenberg, Jenny; Egbert, Nicole; Ploeger, Nicole A; Bernard, Daniel R; King, Shawn

2013-01-01

This study examined the influence of the social networking site Facebook and face-to-face support networks on depression among (N = 361) college students. The authors used the Relational Health Communication Competence Model as a framework for examining the influence of communication competence on social support network satisfaction and depression. Moreover, they examined the influence of interpersonal and social integrative motives as exogenous variables. On the basis of previous work, the authors propose and test a theoretical model using structural equation modeling. The results indicated empirical support for the model, with interpersonal motives predicting increased face-to-face and computer-mediated competence, increased social support satisfaction with face-to-face and Facebook support, and lower depression scores. The implications of the findings for theory, key limitations, and directions for future research are discussed.

Online social networks—Paradise of computer viruses

NASA Astrophysics Data System (ADS)

Fan, W.; Yeung, K. H.

2011-01-01

Online social network services have attracted more and more users in recent years. So the security of social networks becomes a critical problem. In this paper, we propose a virus propagation model based on the application network of Facebook, which is the most popular among these social network service providers. We also study the virus propagation with an email virus model and compare the behaviors of a virus spreading on Facebook with the original email network. It is found that Facebook provides the same chance for a virus spreading while it gives a platform for application developers. And a virus will spread faster in the Facebook network if users of Facebook spend more time on it.

Associating Human-Centered Concepts with Social Networks Using Fuzzy Sets

NASA Astrophysics Data System (ADS)

Yager, Ronald R.

The rapidly growing global interconnectivity, brought about to a large extent by the Internet, has dramatically increased the importance and diversity of social networks. Modern social networks cut across a spectrum from benign recreational focused websites such as Facebook to occupationally oriented websites such as LinkedIn to criminally focused groups such as drug cartels to devastation and terror focused groups such as Al-Qaeda. Many organizations are interested in analyzing and extracting information related to these social networks. Among these are governmental police and security agencies as well marketing and sales organizations. To aid these organizations there is a need for technologies to model social networks and intelligently extract information from these models. While established technologies exist for the modeling of relational networks [1-7] few technologies exist to extract information from these, compatible with human perception and understanding. Data bases is an example of a technology in which we have tools for representing our information as well as tools for querying and extracting the information contained. Our goal is in some sense analogous. We want to use the relational network model to represent information, in this case about relationships and interconnections, and then be able to query the social network using intelligent human-centered concepts. To extend our capabilities to interact with social relational networks we need to associate with these network human concepts and ideas. Since human beings predominantly use linguistic terms in which to reason and understand we need to build bridges between human conceptualization and the formal mathematical representation of the social network. Consider for example a concept such as "leader". An analyst may be able to express, in linguistic terms, using a network relevant vocabulary, properties of a leader. Our task is to translate this linguistic description into a mathematical formalism that allows us to determine how true it is that a particular node is a leader. In this work we look at the use of fuzzy set methodologies [8-10] to provide a bridge between the human analyst and the formal model of the network.

A Social Operational Model of Urban Adolescents' Tobacco and Substance Use: A Mediational Analysis

ERIC Educational Resources Information Center

Mason, Michael J.; Mennis, Jeremy; Schmidt, Christopher D.

2011-01-01

This study tested a mediation model of the relationship with tobacco use, social network quality (level of risk or protection in a network), and substance use (alcohol and/or illicit drugs) with a sample of 301 urban adolescents. It was theorized that social network quality would mediate the effect of tobacco use, accounting for PTSD symptoms and…

Integration of Spatial and Social Network Analysis in Disease Transmission Studies.

PubMed

Emch, Michael; Root, Elisabeth D; Giebultowicz, Sophia; Ali, Mohammad; Perez-Heydrich, Carolina; Yunus, Mohammad

2012-01-01

This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how.

Integration of Spatial and Social Network Analysis in Disease Transmission Studies

PubMed Central

Root, Elisabeth D; Giebultowicz, Sophia; Ali, Mohammad; Perez-Heydrich, Carolina; Yunus, Mohammad

2013-01-01

This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how. PMID:24163443

Technology acceptance perception for promotion of sustainable consumption.

PubMed

Biswas, Aindrila; Roy, Mousumi

2018-03-01

Economic growth in the past decades has resulted in change in consumption pattern and emergence of tech-savvy generation with unprecedented increase in the usage of social network technology. In this paper, the technology acceptance value gap adapted from the technology acceptance model has been applied as a tool supporting social network technology usage and subsequent promotion of sustainable consumption. The data generated through the use of structured questionnaires have been analyzed using structural equation modeling. The validity of the model and path estimates signifies the robustness of Technology Acceptance value gap in adjudicating the efficiency of social network technology usage in augmentation of sustainable consumption and awareness. The results indicate that subjective norm gap, ease-of-operation gap, and quality of green information gap have the most adversarial impact on social network technology usage. Eventually social networking technology usage has been identified as a significant antecedent of sustainable consumption.

Social Network Structures of Breast Cancer Patients and the Contributing Role of Patient Navigators.

PubMed

Gunn, Christine M; Parker, Victoria A; Bak, Sharon M; Ko, Naomi; Nelson, Kerrie P; Battaglia, Tracy A

2017-08-01

Minority women in the U.S. continue to experience inferior breast cancer outcomes compared with white women, in part due to delays in care delivery. Emerging cancer care delivery models like patient navigation focus on social barriers, but evidence demonstrating how these models increase social capital is lacking. This pilot study describes the social networks of newly diagnosed breast cancer patients and explores the contributing role of patient navigators. Twenty-five women completed a one hour interview about their social networks related to cancer care support. Network metrics identified important structural attributes and influential individuals. Bivariate associations between network metrics, type of network, and whether the network included a navigator were measured. Secondary analyses explored associations between network structures and clinical outcomes. We identified three types of networks: kin-based, role and/or affect-based, or heterogeneous. Network metrics did not vary significantly by network type. There was a low prevalence of navigators included in the support networks (25%). Network density scores were significantly higher in those networks without a navigator. Network metrics were not predictive of clinical outcomes in multivariate models. Patient navigators were not frequently included in support networks, but provided distinctive types of support. If navigators can identify patients with poorly integrated (less dense) social networks, or who have unmet tangible support needs, the intensity of navigation services could be tailored. Services and systems that address gaps and variations in patient social networks should be explored for their potential to reduce cancer health disparities. This study used a new method to identify the breadth and strength of social support following a diagnosis of breast cancer, especially examining the role of patient navigators in providing support. While navigators were only included in one quarter of patient support networks, they did provide essential supports to some individuals. Health care providers and systems need to better understand the contributions of social supports both within and outside of health care to design and tailor interventions that seek to reduce health care disparities and improve cancer outcomes. © AlphaMed Press 2017.

Link prediction measures considering different neighbors’ effects and application in social networks

NASA Astrophysics Data System (ADS)

Luo, Peng; Wu, Chong; Li, Yongli

Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.

Entangling mobility and interactions in social media.

PubMed

Grabowicz, Przemyslaw A; Ramasco, José J; Gonçalves, Bruno; Eguíluz, Víctor M

2014-01-01

Daily interactions naturally define social circles. Individuals tend to be friends with the people they spend time with and they choose to spend time with their friends, inextricably entangling physical location and social relationships. As a result, it is possible to predict not only someone's location from their friends' locations but also friendship from spatial and temporal co-occurrence. While several models have been developed to separately describe mobility and the evolution of social networks, there is a lack of studies coupling social interactions and mobility. In this work, we introduce a model that bridges this gap by explicitly considering the feedback of mobility on the formation of social ties. Data coming from three online social networks (Twitter, Gowalla and Brightkite) is used for validation. Our model reproduces various topological and physical properties of the networks not captured by models uncoupling mobility and social interactions such as: i) the total size of the connected components, ii) the distance distribution between connected users, iii) the dependence of the reciprocity on the distance, iv) the variation of the social overlap and the clustering with the distance. Besides numerical simulations, a mean-field approach is also used to study analytically the main statistical features of the networks generated by a simplified version of our model. The robustness of the results to changes in the model parameters is explored, finding that a balance between friend visits and long-range random connections is essential to reproduce the geographical features of the empirical networks.

Networking for philanthropy: increasing volunteer behavior via social networking sites.

PubMed

Kim, Yoojung; Lee, Wei-Na

2014-03-01

Social networking sites (SNSs) provide a unique social venue to engage the young generation in philanthropy through their networking capabilities. An integrated model that incorporates social capital into the Theory of Reasoned Action is developed to explain volunteer behavior through social networks. As expected, volunteer behavior was predicted by volunteer intention, which was influenced by attitudes and subjective norms. In addition, social capital, an outcome of the extensive use of SNSs, was as an important driver of users' attitude and subjective norms toward volunteering via SNSs.

From social integration to health: Durkheim in the new millennium.

PubMed

Berkman, L F; Glass, T; Brissette, I; Seeman, T E

2000-09-01

It is widely recognized that social relationships and affiliation have powerful effects on physical and mental health. When investigators write about the impact of social relationships on health, many terms are used loosely and interchangeably including social networks, social ties and social integration. The aim of this paper is to clarify these terms using a single framework. We discuss: (1) theoretical orientations from diverse disciplines which we believe are fundamental to advancing research in this area; (2) a set of definitions accompanied by major assessment tools; and (3) an overarching model which integrates multilevel phenomena. Theoretical orientations that we draw upon were developed by Durkheim whose work on social integration and suicide are seminal and John Bowlby, a psychiatrist who developed attachment theory in relation to child development and contemporary social network theorists. We present a conceptual model of how social networks impact health. We envision a cascading causal process beginning with the macro-social to psychobiological processes that are dynamically linked together to form the processes by which social integration effects health. We start by embedding social networks in a larger social and cultural context in which upstream forces are seen to condition network structure. Serious consideration of the larger macro-social context in which networks form and are sustained has been lacking in all but a small number of studies and is almost completely absent in studies of social network influences on health. We then move downstream to understand the influences network structure and function have on social and interpersonal behavior. We argue that networks operate at the behavioral level through four primary pathways: (1) provision of social support; (2) social influence; (3) on social engagement and attachment; and (4) access to resources and material goods.

Gossip spread in social network Models

NASA Astrophysics Data System (ADS)

Johansson, Tobias

2017-04-01

Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.

Modeling and analyzing malware propagation in social networks with heterogeneous infection rates

NASA Astrophysics Data System (ADS)

Jia, Peng; Liu, Jiayong; Fang, Yong; Liu, Liang; Liu, Luping

2018-10-01

With the rapid development of social networks, hackers begin to try to spread malware more widely by utilizing various kinds of social networks. Thus, studying malware epidemic dynamics in these networks is becoming a popular subject in the literature. Most of the previous works focus on the effects of factors, such as network topology and user behavior, on malware propagation. Some researchers try to analyze the heterogeneity of infection rates, but the common problem of their works is the factors they mentioned that could affect the heterogeneity are not comprehensive enough. In this paper, focusing on the effects of heterogeneous infection rates, we propose a novel model called HSID (heterogeneous-susceptible-infectious-dormant model) to characterize virus propagation in social networks, in which a connection factor is presented to evaluate the heterogeneous relationships between nodes, and a resistance factor is introduced to represent node's mutable resistant ability. We analyzed how key parameters in the two factors affect the heterogeneity and then performed simulations to explore the effects in three real-world social networks. The results indicate: heterogeneous relationship could lead to wider diffusion in directed network, and heterogeneous security awareness could lead to wider diffusion in both directed and undirected networks; heterogeneous relationship could restrain the outbreak of malware but heterogeneous initial security awareness would increase the probability; furthermore, the increasing resistibility along with infected times would lead to malware's disappearance in social networks.

Modeling cascading failures with the crisis of trust in social networks

NASA Astrophysics Data System (ADS)

Yi, Chengqi; Bao, Yuanyuan; Jiang, Jingchi; Xue, Yibo

2015-10-01

In social networks, some friends often post or disseminate malicious information, such as advertising messages, informal overseas purchasing messages, illegal messages, or rumors. Too much malicious information may cause a feeling of intense annoyance. When the feeling exceeds a certain threshold, it will lead social network users to distrust these friends, which we call the crisis of trust. The crisis of trust in social networks has already become a universal concern and an urgent unsolved problem. As a result of the crisis of trust, users will cut off their relationships with some of their untrustworthy friends. Once a few of these relationships are made unavailable, it is likely that other friends will decline trust, and a large portion of the social network will be influenced. The phenomenon in which the unavailability of a few relationships will trigger the failure of successive relationships is known as cascading failure dynamics. To our best knowledge, no one has formally proposed cascading failures dynamics with the crisis of trust in social networks. In this paper, we address this potential issue, quantify the trust between two users based on user similarity, and model the minimum tolerance with a nonlinear equation. Furthermore, we construct the processes of cascading failures dynamics by considering the unique features of social networks. Based on real social network datasets (Sina Weibo, Facebook and Twitter), we adopt two attack strategies (the highest trust attack (HT) and the lowest trust attack (LT)) to evaluate the proposed dynamics and to further analyze the changes of the topology, connectivity, cascading time and cascade effect under the above attacks. We numerically find that the sparse and inhomogeneous network structure in our cascading model can better improve the robustness of social networks than the dense and homogeneous structure. However, the network structure that seems like ripples is more vulnerable than the other two network structures. Our findings will be useful in further guiding the construction of social networks to effectively avoid the cascading propagation with the crisis of trust. Some research results can help social network service providers to avoid severe cascading failures.

a New Dynamic Community Model for Social Networks

NASA Astrophysics Data System (ADS)

Lu, Zhe-Ming; Wu, Zhen; Guo, Shi-Ze; Chen, Zhe; Song, Guang-Hua

2014-09-01

In this paper, based on the phenomenon that individuals join into and jump from the organizations in the society, we propose a dynamic community model to construct social networks. Two parameters are adopted in our model, one is the communication rate Pa that denotes the connection strength in the organization and the other is the turnover rate Pb, that stands for the frequency of jumping among the organizations. Based on simulations, we analyze not only the degree distribution, the clustering coefficient, the average distance and the network diameter but also the group distribution which is closely related to their community structure. Moreover, we discover that the networks generated by the proposed model possess the small-world property and can well reproduce the networks of social contacts.

Identifying and tracking dynamic processes in social networks

NASA Astrophysics Data System (ADS)

Chung, Wayne; Savell, Robert; Schütt, Jan-Peter; Cybenko, George

2006-05-01

The detection and tracking of embedded malicious subnets in an active social network can be computationally daunting due to the quantity of transactional data generated in the natural interaction of large numbers of actors comprising a network. In addition, detection of illicit behavior may be further complicated by evasive strategies designed to camouflage the activities of the covert subnet. In this work, we move beyond traditional static methods of social network analysis to develop a set of dynamic process models which encode various modes of behavior in active social networks. These models will serve as the basis for a new application of the Process Query System (PQS) to the identification and tracking of covert dynamic processes in social networks. We present a preliminary result from application of our technique in a real-world data stream-- the Enron email corpus.

Social Network Assessments and Interventions for Health Behavior Change: A Critical Review.

PubMed

Latkin, Carl A; Knowlton, Amy R

2015-01-01

Social networks provide a powerful approach for health behavior change. This article documents how social network interventions have been successfully used for a range of health behaviors, including HIV risk practices, smoking, exercise, dieting, family planning, bullying, and mental health. We review the literature that suggests the relationship between health behaviors and social network attributes demonstrates a high degree of specificity. The article then examines hypothesized social influence mechanisms including social norms, modeling, and social rewards and the factors of social identity and social rewards that can be employed to sustain social network interventions. Areas of future research avenues are highlighted, including the need to examine and to adjust analytically for contamination and social diffusion, social influence versus differential affiliation, and network change. Use and integration of mhealth and face-to-face networks for promoting health behavior change are also critical research areas.

Social Protocols for Agile Virtual Teams

NASA Astrophysics Data System (ADS)

Picard, Willy

Despite many works on collaborative networked organizations (CNOs), CSCW, groupware, workflow systems and social networks, computer support for virtual teams is still insufficient, especially support for agility, i.e. the capability of virtual team members to rapidly and cost efficiently adapt the way they interact to changes. In this paper, requirements for computer support for agile virtual teams are presented. Next, an extension of the concept of social protocol is proposed as a novel model supporting agile interactions within virtual teams. The extended concept of social protocol consists of an extended social network and a workflow model.

Emergence, evolution and scaling of online social networks.

PubMed

Wang, Le-Zhi; Huang, Zi-Gang; Rong, Zhi-Hai; Wang, Xiao-Fan; Lai, Ying-Cheng

2014-01-01

Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China) with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.

Reinforced communication and social navigation: Remember your friends and remember yourself

NASA Astrophysics Data System (ADS)

Mirshahvalad, A.; Rosvall, M.

2011-09-01

In social systems, people communicate with each other and form groups based on their interests. The pattern of interactions, the network, and the ideas that flow on the network naturally evolve together. Researchers use simple models to capture the feedback between changing network patterns and ideas on the network, but little is understood about the role of past events in the feedback process. Here, we introduce a simple agent-based model to study the coupling between peoples’ ideas and social networks, and better understand the role of history in dynamic social networks. We measure how information about ideas can be recovered from information about network structure and, the other way around, how information about network structure can be recovered from information about ideas. We find that it is, in general, easier to recover ideas from the network structure than vice versa.

Coevolutionary, coexisting learning and teaching agents model for prisoner’s dilemma games enhancing cooperation with assortative heterogeneous networks

NASA Astrophysics Data System (ADS)

Tanimoto, Jun

2013-07-01

Unlike other natural network systems, assortativity can be observed in most human social networks, although it has been reported that a social dilemma situation represented by the prisoner’s dilemma favors dissortativity to enhance cooperation. We established a new coevolutionary model for both agents’ strategy and network topology, where teaching and learning agents coexist. Remarkably, this model enables agents’ enhancing cooperation more than a learners-only model on a time-frozen scale-free network and produces an underlying assortative network with a fair degree of power-law distribution. The model may imply how and why assortative networks are adaptive in human society.

Wild cricket social networks show stability across generations.

PubMed

Fisher, David N; Rodríguez-Muñoz, Rolando; Tregenza, Tom

2016-07-27

A central part of an animal's environment is its interactions with conspecifics. There has been growing interest in the potential to capture these interactions in the form of a social network. Such networks can then be used to examine how relationships among individuals affect ecological and evolutionary processes. However, in the context of selection and evolution, the utility of this approach relies on social network structures persisting across generations. This is an assumption that has been difficult to test because networks spanning multiple generations have not been available. We constructed social networks for six annual generations over a period of eight years for a wild population of the cricket Gryllus campestris. Through the use of exponential random graph models (ERGMs), we found that the networks in any given year were able to predict the structure of networks in other years for some network characteristics. The capacity of a network model of any given year to predict the networks of other years did not depend on how far apart those other years were in time. Instead, the capacity of a network model to predict the structure of a network in another year depended on the similarity in population size between those years. Our results indicate that cricket social network structure resists the turnover of individuals and is stable across generations. This would allow evolutionary processes that rely on network structure to take place. The influence of network size may indicate that scaling up findings on social behaviour from small populations to larger ones will be difficult. Our study also illustrates the utility of ERGMs for comparing networks, a task for which an effective approach has been elusive.

Social media: opportunities for quality improvement and lessons for providers-a networked model for patient-centered care through digital engagement.

PubMed

Bornkessel, Alexandra; Furberg, Robert; Lefebvre, R Craig

2014-07-01

Social media brings a new dimension to health care for patients, providers, and their support networks. Increasing evidence demonstrates that patients who are more actively involved in their healthcare experience have better health outcomes and incur lower costs. In the field of cardiology, social media are proposed as innovative tools for the education and update of clinicians, physicians, nurses, and medical students. This article reviews the use of social media by healthcare providers and patients and proposes a model of "networked care" that integrates the use of digital social networks and platforms by both patients and providers and offers recommendations for providers to optimize their use and understanding of social media for quality improvement.

Modeling the effect of social networks on adoption of multifunctional agriculture.

PubMed

Manson, Steven M; Jordan, Nicholas R; Nelson, Kristen C; Brummel, Rachel F

2016-01-01

Rotational grazing (RG) has attracted much attention as a cornerstone of multifunctional agriculture (MFA) in animal systems, potentially capable of producing a range of goods and services of value to diverse stakeholders in agricultural landscapes and rural communities, as well as broader societal benefits. Despite these benefits, global adoption of MFA has been uneven, with some places seeing active participation, while others have seen limited growth. Recent conceptual models of MFA emphasize the potential for bottom-up processes and linkages among social and environmental systems to promote multifunctionality. Social networks are critical to these explanations but how and why these networks matter is unclear. We investigated fifty-three farms in three states in the United States (New York, Wisconsin, Pennsylvania) and developed a stylized model of social networks and systemic change in the dairy farming system. We found that social networks are important to RG adoption but their impact is contingent on social and spatial factors. Effects of networks on farmer decision making differ according to whether they comprise weak-tie relationships, which bridge across disparate people and organizations, or strong-tie relationships, which are shared by groups in which members are well known to one another. RG adoption is also dependent on features of the social landscape including the number of dairy households, the probability of neighboring farmers sharing strong ties, and the role of space in how networks are formed. The model replicates features of real-world adoption of RG practices in the Eastern US and illustrates pathways toward greater multifunctionality in the dairy landscape. Such models are likely to be of heuristic value in network-focused strategies for agricultural development.

Modeling the effect of social networks on adoption of multifunctional agriculture

PubMed Central

Manson, Steven M.; Jordan, Nicholas R.; Nelson, Kristen C.; Brummel, Rachel F.

2014-01-01

Rotational grazing (RG) has attracted much attention as a cornerstone of multifunctional agriculture (MFA) in animal systems, potentially capable of producing a range of goods and services of value to diverse stakeholders in agricultural landscapes and rural communities, as well as broader societal benefits. Despite these benefits, global adoption of MFA has been uneven, with some places seeing active participation, while others have seen limited growth. Recent conceptual models of MFA emphasize the potential for bottom-up processes and linkages among social and environmental systems to promote multifunctionality. Social networks are critical to these explanations but how and why these networks matter is unclear. We investigated fifty-three farms in three states in the United States (New York, Wisconsin, Pennsylvania) and developed a stylized model of social networks and systemic change in the dairy farming system. We found that social networks are important to RG adoption but their impact is contingent on social and spatial factors. Effects of networks on farmer decision making differ according to whether they comprise weak-tie relationships, which bridge across disparate people and organizations, or strong-tie relationships, which are shared by groups in which members are well known to one another. RG adoption is also dependent on features of the social landscape including the number of dairy households, the probability of neighboring farmers sharing strong ties, and the role of space in how networks are formed. The model replicates features of real-world adoption of RG practices in the Eastern US and illustrates pathways toward greater multifunctionality in the dairy landscape. Such models are likely to be of heuristic value in network-focused strategies for agricultural development. PMID:26744579

Social network supported process recommender system.

PubMed

Ye, Yanming; Yin, Jianwei; Xu, Yueshen

2014-01-01

Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced.

Dynamic Evolution Model Based on Social Network Services

NASA Astrophysics Data System (ADS)

Xiong, Xi; Gou, Zhi-Jian; Zhang, Shi-Bin; Zhao, Wen

2013-11-01

Based on the analysis of evolutionary characteristics of public opinion in social networking services (SNS), in the paper we propose a dynamic evolution model, in which opinions are coupled with topology. This model shows the clustering phenomenon of opinions in dynamic network evolution. The simulation results show that the model can fit the data from a social network site. The dynamic evolution of networks accelerates the opinion, separation and aggregation. The scale and the number of clusters are influenced by confidence limit and rewiring probability. Dynamic changes of the topology reduce the number of isolated nodes, while the increased confidence limit allows nodes to communicate more sufficiently. The two effects make the distribution of opinion more neutral. The dynamic evolution of networks generates central clusters with high connectivity and high betweenness, which make it difficult to control public opinions in SNS.

Social Insects: A Model System for Network Dynamics

NASA Astrophysics Data System (ADS)

Charbonneau, Daniel; Blonder, Benjamin; Dornhaus, Anna

Social insect colonies (ants, bees, wasps, and termites) show sophisticated collective problem-solving in the face of variable constraints. Individuals exchange information and materials such as food. The resulting network structure and dynamics can inform us about the mechanisms by which the insects achieve particular collective behaviors and these can be transposed to man-made and social networks. We discuss how network analysis can answer important questions about social insects, such as how effective task allocation or information flow is realized. We put forward the idea that network analysis methods are under-utilized in social insect research, and that they can provide novel ways to view the complexity of collective behavior, particularly if network dynamics are taken into account. To illustrate this, we present an example of network tasks performed by ant workers, linked by instances of workers switching from one task to another. We show how temporal network analysis can propose and test new hypotheses on mechanisms of task allocation, and how adding temporal elements to static networks can drastically change results. We discuss the benefits of using social insects as models for complex systems in general. There are multiple opportunities emergent technologies and analysis methods in facilitating research on social insect network. The potential for interdisciplinary work could significantly advance diverse fields such as behavioral ecology, computer sciences, and engineering.

A generalized theory of preferential linking

NASA Astrophysics Data System (ADS)

Hu, Haibo; Guo, Jinli; Liu, Xuan; Wang, Xiaofan

2014-12-01

There are diverse mechanisms driving the evolution of social networks. A key open question dealing with understanding their evolution is: How do various preferential linking mechanisms produce networks with different features? In this paper we first empirically study preferential linking phenomena in an evolving online social network, find and validate the linear preference. We propose an analyzable model which captures the real growth process of the network and reveals the underlying mechanism dominating its evolution. Furthermore based on preferential linking we propose a generalized model reproducing the evolution of online social networks, and present unified analytical results describing network characteristics for 27 preference scenarios. We study the mathematical structure of degree distributions and find that within the framework of preferential linking analytical degree distributions can only be the combinations of finite kinds of functions which are related to rational, logarithmic and inverse tangent functions, and extremely complex network structure will emerge even for very simple sublinear preferential linking. This work not only provides a verifiable origin for the emergence of various network characteristics in social networks, but bridges the micro individuals' behaviors and the global organization of social networks.

Investigation of Social Studies Teachers' Intended Uses of Social Networks in Terms of Various Variables

ERIC Educational Resources Information Center

Akgün, Ismail Hakan

2016-01-01

The aim of this research is to determine Social Studies teacher candidates' intended uses of social networks in terms of various variables. The research was carried out by using screening model of quantitative research methods. In the study, "The Social Network Intended Use Scale" was used as a data collection tool. As a result of the…

The influence of tie strength on evolutionary games on networks: An empirical investigation

NASA Astrophysics Data System (ADS)

Buesser, Pierre; Peña, Jorge; Pestelacci, Enea; Tomassini, Marco

2011-11-01

Extending previous work on unweighted networks, we present here a systematic numerical investigation of standard evolutionary games on weighted networks. In the absence of any reliable model for generating weighted social networks, we attribute weights to links in a few ways supported by empirical data ranging from totally uncorrelated to weighted bipartite networks. The results of the extensive simulation work on standard complex network models show that, except in a case that does not seem to be common in social networks, taking the tie strength into account does not change in a radical manner the long-run steady-state behavior of the studied games. Besides model networks, we also included a real-life case drawn from a coauthorship network. In this case also, taking the weights into account only changes the results slightly with respect to the raw unweighted graph, although to draw more reliable conclusions on real social networks many more cases should be studied as these weighted networks become available.

Hierarchical Network Models for Education Research: Hierarchical Latent Space Models

ERIC Educational Resources Information Center

Sweet, Tracy M.; Thomas, Andrew C.; Junker, Brian W.

2013-01-01

Intervention studies in school systems are sometimes aimed not at changing curriculum or classroom technique, but rather at changing the way that teachers, teaching coaches, and administrators in schools work with one another--in short, changing the professional social networks of educators. Current methods of social network analysis are…

Information Diffusion in Facebook-Like Social Networks Under Information Overload

NASA Astrophysics Data System (ADS)

Li, Pei; Xing, Kai; Wang, Dapeng; Zhang, Xin; Wang, Hui

2013-07-01

Research on social networks has received remarkable attention, since many people use social networks to broadcast information and stay connected with their friends. However, due to the information overload in social networks, it becomes increasingly difficult for users to find useful information. This paper takes Facebook-like social networks into account, and models the process of information diffusion under information overload. The term view scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated is proposed to characterize the information diffusion efficiency. Through theoretical analysis, we find that factors such as network structure and view scope number have no impact on the information diffusion efficiency, which is a surprising result. To verify the results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly.

Active influence in dynamical models of structural balance in social networks

NASA Astrophysics Data System (ADS)

Summers, Tyler H.; Shames, Iman

2013-07-01

We consider a nonlinear dynamical system on a signed graph, which can be interpreted as a mathematical model of social networks in which the links can have both positive and negative connotations. In accordance with a concept from social psychology called structural balance, the negative links play a key role in both the structure and dynamics of the network. Recent research has shown that in a nonlinear dynamical system modeling the time evolution of “friendliness levels” in the network, two opposing factions emerge from almost any initial condition. Here we study active external influence in this dynamical model and show that any agent in the network can achieve any desired structurally balanced state from any initial condition by perturbing its own local friendliness levels. Based on this result, we also introduce a new network centrality measure for signed networks. The results are illustrated in an international-relations network using United Nations voting record data from 1946 to 2008 to estimate friendliness levels amongst various countries.

Competition between global and local online social networks

NASA Astrophysics Data System (ADS)

Kleineberg, Kaj-Kolja; Boguñá, Marián

2016-04-01

The overwhelming success of online social networks, the key actors in the Web 2.0 cosmos, has reshaped human interactions globally. To help understand the fundamental mechanisms which determine the fate of online social networks at the system level, we describe the digital world as a complex ecosystem of interacting networks. In this paper, we study the impact of heterogeneity in network fitnesses on the competition between an international network, such as Facebook, and local services. The higher fitness of international networks is induced by their ability to attract users from all over the world, which can then establish social interactions without the limitations of local networks. In other words, inter-country social ties lead to increased fitness of the international network. To study the competition between an international network and local ones, we construct a 1:1000 scale model of the digital world, consisting of the 80 countries with the most Internet users. Under certain conditions, this leads to the extinction of local networks; whereas under different conditions, local networks can persist and even dominate completely. In particular, our model suggests that, with the parameters that best reproduce the empirical overtake of Facebook, this overtake could have not taken place with a significant probability.

Competition between global and local online social networks.

PubMed

Kleineberg, Kaj-Kolja; Boguñá, Marián

2016-04-27

The overwhelming success of online social networks, the key actors in the Web 2.0 cosmos, has reshaped human interactions globally. To help understand the fundamental mechanisms which determine the fate of online social networks at the system level, we describe the digital world as a complex ecosystem of interacting networks. In this paper, we study the impact of heterogeneity in network fitnesses on the competition between an international network, such as Facebook, and local services. The higher fitness of international networks is induced by their ability to attract users from all over the world, which can then establish social interactions without the limitations of local networks. In other words, inter-country social ties lead to increased fitness of the international network. To study the competition between an international network and local ones, we construct a 1:1000 scale model of the digital world, consisting of the 80 countries with the most Internet users. Under certain conditions, this leads to the extinction of local networks; whereas under different conditions, local networks can persist and even dominate completely. In particular, our model suggests that, with the parameters that best reproduce the empirical overtake of Facebook, this overtake could have not taken place with a significant probability.

Modeling Temporal Variation in Social Network: An Evolutionary Web Graph Approach

NASA Astrophysics Data System (ADS)

Mitra, Susanta; Bagchi, Aditya

A social network is a social structure between actors (individuals, organization or other social entities) and indicates the ways in which they are connected through various social relationships like friendships, kinships, professional, academic etc. Usually, a social network represents a social community, like a club and its members or a city and its citizens etc. or a research group communicating over Internet. In seventies Leinhardt [1] first proposed the idea of representing a social community by a digraph. Later, this idea became popular among other research workers like, network designers, web-service application developers and e-learning modelers. It gave rise to a rapid proliferation of research work in the area of social network analysis. Some of the notable structural properties of a social network are connectedness between actors, reachability between a source and a target actor, reciprocity or pair-wise connection between actors with bi-directional links, centrality of actors or the important actors having high degree or more connections and finally the division of actors into sub-structures or cliques or strongly-connected components. The cycles present in a social network may even be nested [2, 3]. The formal definition of these structural properties will be provided in Sect. 8.2.1. The division of actors into cliques or sub-groups can be a very important factor for understanding a social structure, particularly the degree of cohesiveness in a community. The number, size, and connections among the sub-groups in a network are useful in understanding how the network, as a whole, is likely to behave.

Dynamic social networks facilitate cooperation in the N-player Prisoner’s Dilemma

NASA Astrophysics Data System (ADS)

Rezaei, Golriz; Kirley, Michael

2012-12-01

Understanding how cooperative behaviour evolves in network communities, where the individual members interact via social dilemma games, is an on-going challenge. In this paper, we introduce a social network based model to investigate the evolution of cooperation in the N-player Prisoner’s Dilemma game. As such, this work complements previous studies focused on multi-player social dilemma games and endogenous networks. Agents in our model, employ different game-playing strategies reflecting varying cognitive capacities. When an agent plays cooperatively, a social link is formed with each of the other N-1 group members. Subsequent cooperative actions reinforce this link. However, when an agent defects, the links in the social network are broken. Computational simulations across a range of parameter settings are used to examine different scenarios: varying population and group sizes; the group formation process (or partner selection); and agent decision-making strategies under varying dilemma constraints (cost-to-benefit ratios), including a “discriminator” strategy where the action is based on a function of the weighted links within an agent’s social network. The simulation results show that the proposed social network model is able to evolve and maintain cooperation. As expected, as the value of N increases the equilibrium proportion of cooperators in the population decreases. In addition, this outcome is dependent on the dilemma constraint (cost-to-benefit ratio). However, in some circumstances the dynamic social network plays an increasingly important role in promoting and sustaining cooperation, especially when the agents adopt the discriminator strategy. The adjustment of social links results in the formation of communities of “like-minded” agents. Subsequently, this local optimal behaviour promotes the evolution of cooperative behaviour at the system level.

How multiple social networks affect user awareness: The information diffusion process in multiplex networks

NASA Astrophysics Data System (ADS)

Li, Weihua; Tang, Shaoting; Fang, Wenyi; Guo, Quantong; Zhang, Xiao; Zheng, Zhiming

2015-10-01

The information diffusion process in single complex networks has been extensively studied, especially for modeling the spreading activities in online social networks. However, individuals usually use multiple social networks at the same time, and can share the information they have learned from one social network to another. This phenomenon gives rise to a new diffusion process on multiplex networks with more than one network layer. In this paper we account for this multiplex network spreading by proposing a model of information diffusion in two-layer multiplex networks. We develop a theoretical framework using bond percolation and cascading failure to describe the intralayer and interlayer diffusion. This allows us to obtain analytical solutions for the fraction of informed individuals as a function of transmissibility T and the interlayer transmission rate θ . Simulation results show that interaction between layers can greatly enhance the information diffusion process. And explosive diffusion can occur even if the transmissibility of the focal layer is under the critical threshold, due to interlayer transmission.

Propagation, cascades, and agreement dynamics in complex communication and social networks

NASA Astrophysics Data System (ADS)

Lu, Qiming

Many modern and important technological, social, information and infrastructure systems can be viewed as complex systems with a large number of interacting components. Models of complex networks and dynamical interactions, as well as their applications are of fundamental interests in many aspects. Here, several stylized models of multiplex propagation and opinion dynamics are investigated on complex and empirical social networks. We first investigate cascade dynamics in threshold-controlled (multiplex) propagation on random geometric networks. We find that such local dynamics can serve as an efficient, robust, and reliable prototypical activation protocol in sensor networks in responding to various alarm scenarios. We also consider the same dynamics on a modified network by adding a few long-range communication links, resulting in a small-world network. We find that such construction can further enhance and optimize the speed of the network's response, while keeping energy consumption at a manageable level. We also investigate a prototypical agent-based model, the Naming Game, on two-dimensional random geometric networks. The Naming Game [A. Baronchelli et al., J. Stat. Mech.: Theory Exp. (2006) P06014.] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the Naming Games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially-embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long-range communication links are added on top of the random geometric graph, resulting in a "small-world"-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case. When applying the model of Naming Game on empirical social networks, this stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.

Mathematical modelling of complex contagion on clustered networks

NASA Astrophysics Data System (ADS)

O'sullivan, David J.; O'Keeffe, Gary; Fennell, Peter; Gleeson, James

2015-09-01

The spreading of behavior, such as the adoption of a new innovation, is influenced bythe structure of social networks that interconnect the population. In the experiments of Centola (Science, 2010), adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the “complex contagion” effects of social reinforcement are important in such diffusion, in contrast to “simple” contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g. of mean-field theory) regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hebert-Dufresne et al. (Phys. Rev. E, 2010), to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola’s experiments—faster diffusion on clustered topologies than on random networks.

The social network index and its relation to later-life depression among the elderly aged ≥80 years in Northern Thailand.

PubMed

Aung, Myo Nyein; Moolphate, Saiyud; Aung, Thin Nyein Nyein; Katonyoo, Chitima; Khamchai, Songyos; Wannakrairot, Pongsak

2016-01-01

Having a diverse social network is considered to be beneficial to a person's well-being. The significance, however, of social network diversity in the geriatric assessment of people aged ≥80 years has not been adequately investigated within the Southeast Asian context. This study explored the social networks belonging to the elderly aged ≥80 years and assessed the relation of social network and geriatric depression. This study was a community-based cross-sectional survey conducted in Chiang Mai Province, Northern Thailand. A representative sample of 435 community residents, aged ≥80 years, were included in a multistage sample. The participants' social network diversity was assessed by applying Cohen's social network index (SNI). The geriatric depression scale and activities of daily living measures were carried out during home visits. Descriptive analyses revealed the distribution of SNI, while the relationship between the SNI and the geriatric depression scale was examined by ordinal logistic regression models controlling possible covariants such as age, sex, and educational attainment. The median age of the sample was 83 years, with females comprising of 54.94% of the sample. The participants' children, their neighbors, and members of Buddhist temples were reported as the most frequent contacts of the study participants. Among the 435 participants, 25% were at risk of social isolation due to having a "limited" social network group (SNI 0-3), whereas 37% had a "medium" social network (SNI 4-5), and 38% had a "diverse" social network (SNI ≥6). The SNI was not different among the two sexes. Activities of daily living scores in the diverse social network group were significantly higher than those in the limited social network group. Multivariate ordinal logistic regression analysis models revealed a significant negative association between social network diversity and geriatric depression. Regular and frequent contact with various social contacts may safeguard common geriatric depression among persons aged ≥80 years. As a result, screening those at risk of social isolation is recommended to be integrated into routine primary health care-based geriatric assessment and intervention programs.

Finding meaning in social media: content-based social network analysis of QuitNet to identify new opportunities for health promotion.

PubMed

Myneni, Sahiti; Cobb, Nathan K; Cohen, Trevor

2013-01-01

Unhealthy behaviors increase individual health risks and are a socioeconomic burden. Harnessing social influence is perceived as fundamental for interventions to influence health-related behaviors. However, the mechanisms through which social influence occurs are poorly understood. Online social networks provide the opportunity to understand these mechanisms as they digitally archive communication between members. In this paper, we present a methodology for content-based social network analysis, combining qualitative coding, automated text analysis, and formal network analysis such that network structure is determined by the content of messages exchanged between members. We apply this approach to characterize the communication between members of QuitNet, an online social network for smoking cessation. Results indicate that the method identifies meaningful theme-based social sub-networks. Modeling social network data using this method can provide us with theme-specific insights such as the identities of opinion leaders and sub-community clusters. Implications for design of targeted social interventions are discussed.

Models, Entropy and Information of Temporal Social Networks

NASA Astrophysics Data System (ADS)

Zhao, Kun; Karsai, Márton; Bianconi, Ginestra

Temporal social networks are characterized by heterogeneous duration of contacts, which can either follow a power-law distribution, such as in face-to-face interactions, or a Weibull distribution, such as in mobile-phone communication. Here we model the dynamics of face-to-face interaction and mobile phone communication by a reinforcement dynamics, which explains the data observed in these different types of social interactions. We quantify the information encoded in the dynamics of these networks by the entropy of temporal networks. Finally, we show evidence that human dynamics is able to modulate the information present in social network dynamics when it follows circadian rhythms and when it is interfacing with a new technology such as the mobile-phone communication technology.

Family ties: the multilevel effects of households and kinship on the networks of individuals.

PubMed

Koster, Jeremy

2018-04-01

Among social mammals, humans uniquely organize themselves into communities of households that are centred around enduring, predominantly monogamous unions of men and women. As a consequence of this social organization, individuals maintain social relationships both within and across households, and potentially there is conflict among household members about which social ties to prioritize or de-emphasize. Extending the logic of structural balance theory, I predict that there will be considerable overlap in the social networks of individual household members, resulting in a pattern of group-level reciprocity. To test this prediction, I advance the Group-Structured Social Relations Model, a generalized linear mixed model that tests for group-level effects in the inter-household social networks of individuals. The empirical data stem from social support interviews conducted in a community of indigenous Nicaraguan horticulturalists, and model results show high group-level reciprocity among households. Although support networks are organized around kinship, covariates that test predictions of kin selection models do not receive strong support, potentially because most kin-directed altruism occurs within households, not between households. In addition, the models show that households with high genetic relatedness in part from children born to adulterous relationships are less likely to assist each other.

Bidirectional influence: A longitudinal analysis of size of drug network and depression among inner-city residents in Baltimore, Maryland

PubMed Central

Yang, Jingyan; Latkin, Carl A.; Davey-Rothwell, Melissa

2015-01-01

BACKGROUND The prevalence of depression among drug users is high. It has been recognized that drug use behaviors can be influenced and spread through social networks. OBJECTIVES We investigated the directional relationship between social network factors and depressive symptoms among a sample of inner-city residents in Baltimore, MD. METHODS We performed a longitudinal study of four-wave data collected from a network-based HIV/STI prevention intervention for women and network members, consisting of both men and women. Our primary outcome and exposure were depression using CESD scale and social network characteristics, respectively. Linear mixed model with clustering adjustment was used to account for both repeated measurement and network design. RESULTS Of the 746 participants, those who had high levels of depression tended to be female, less educated, homeless, smokers, and did not have a main partner. In the univariate longitudinal model, larger size of drug network was significantly associated with depression (OR=1.38, p<0.001). This relationship held after controlling for age, gender, homeless in the past six months, college education, having a main partner, cigarette smoking, perceived health, and social support network (aOR=1.19, p=0.001). In the univariate mixed model using depression to predict size of drug network, the data suggested that depression was associated with larger size of drug network (coef.=1.23, p<0.001) and the same relation held in multivariate model (adjusted coef.=1.08, p=0.001). CONCLUSIONS The results suggest that larger size of drug network is a risk factor for depression, and vice versa. Further intervention strategies to reduce depression should address social networks factors. PMID:26584046

Involvement, Collaboration and Engagement: Social Networks through a Pedagogical Lens

ERIC Educational Resources Information Center

Seifert, Tami

2016-01-01

Social networks facilitate activities that promote involvement, collaboration and engagement. Modelling of best practices using social networks enhances its usage by participants, increases participants confidence as to its implementation and creates a paradigm shift to a more personalized, participatory and collaborative learning and a more…

Social Network Supported Process Recommender System

PubMed Central

Ye, Yanming; Yin, Jianwei; Xu, Yueshen

2014-01-01

Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced. PMID:24672309

Two classes of bipartite networks: nested biological and social systems.

PubMed

Burgos, Enrique; Ceva, Horacio; Hernández, Laura; Perazzo, R P J; Devoto, Mariano; Medan, Diego

2008-10-01

Bipartite graphs have received some attention in the study of social networks and of biological mutualistic systems. A generalization of a previous model is presented, that evolves the topology of the graph in order to optimally account for a given contact preference rule between the two guilds of the network. As a result, social and biological graphs are classified as belonging to two clearly different classes. Projected graphs, linking the agents of only one guild, are obtained from the original bipartite graph. The corresponding evolution of its statistical properties is also studied. An example of a biological mutualistic network is analyzed in detail, and it is found that the model provides a very good fitting of all the main statistical features. The model also provides a proper qualitative description of the same features observed in social webs, suggesting the possible reasons underlying the difference in the organization of these two kinds of bipartite networks.

Power to Detect Intervention Effects on Ensembles of Social Networks

ERIC Educational Resources Information Center

Sweet, Tracy M.; Junker, Brian W.

2016-01-01

The hierarchical network model (HNM) is a framework introduced by Sweet, Thomas, and Junker for modeling interventions and other covariate effects on ensembles of social networks, such as what would be found in randomized controlled trials in education research. In this article, we develop calculations for the power to detect an intervention…

Social network analysis: Presenting an underused method for nursing research.

PubMed

Parnell, James Michael; Robinson, Jennifer C

2018-06-01

This paper introduces social network analysis as a versatile method with many applications in nursing research. Social networks have been studied for years in many social science fields. The methods continue to advance but remain unknown to most nursing scholars. Discussion paper. English language and interpreted literature was searched from Ovid Healthstar, CINAHL, PubMed Central, Scopus and hard copy texts from 1965 - 2017. Social network analysis first emerged in nursing literature in 1995 and appears minimally through present day. To convey the versatility and applicability of social network analysis in nursing, hypothetical scenarios are presented. The scenarios are illustrative of three approaches to social network analysis and include key elements of social network research design. The methods of social network analysis are underused in nursing research, primarily because they are unknown to most scholars. However, there is methodological flexibility and epistemological versatility capable of supporting quantitative and qualitative research. The analytic techniques of social network analysis can add new insight into many areas of nursing inquiry, especially those influenced by cultural norms. Furthermore, visualization techniques associated with social network analysis can be used to generate new hypotheses. Social network analysis can potentially uncover findings not accessible through methods commonly used in nursing research. Social networks can be analysed based on individual-level attributes, whole networks and subgroups within networks. Computations derived from social network analysis may stand alone to answer a research question or incorporated as variables into robust statistical models. © 2018 John Wiley & Sons Ltd.

Social class shapes the form and function of relationships and selves.

PubMed

Carey, Rebecca M; Markus, Hazel Rose

2017-12-01

Social class shapes relational realities, which in turn situate and structure different selves and their associated psychological tendencies. We first briefly review how higher class contexts tend to foster independent models of self and lower class contexts tend to foster interdependent models of self. We then consider how these independent and interdependent models of self are situated in and adapted to different social class-driven relational realities. We review research demonstrating that in lower social class contexts, social networks tend to be small, dense, homogenous and strongly connected. Ties in these networks provide the bonding capital that is key for survival and that promotes the interdependence between self and other(s). In higher social class contexts, social networks tend to be large, far-reaching, diverse and loosely connected. Ties in these networks provide the bridging capital that is key for achieving personal goals and that promotes an independence of self from other. We conclude that understanding and addressing issues tied to social class and inequality requires understanding the form and function of relationships across class contexts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Smart social adaptation prevents catastrophic ecological regime shifts in networks of myopic harvesters

NASA Astrophysics Data System (ADS)

Donges, Jonathan; Lucht, Wolfgang; Wiedermann, Marc; Heitzig, Jobst; Kurths, Jürgen

2015-04-01

In the anthropocene, the rise of global social and economic networks with ever increasing connectivity and speed of interactions, e.g., the internet or global financial markets, is a key challenge for sustainable development. The spread of opinions, values or technologies on these networks, in conjunction with the coevolution of the network structures themselves, underlies nexuses of current concern such as anthropogenic climate change, biodiversity loss or global land use change. To isolate and quantitatively study the effects and implications of network dynamics for sustainable development, we propose an agent-based model of information flow on adaptive networks between myopic harvesters that exploit private renewable resources. In this conceptual model of a network of socio-ecological systems, information on management practices flows between agents via boundedly rational imitation depending on the state of the resource stocks involved in an interaction. Agents can also adapt the structure of their social network locally by preferentially connecting to culturally similar agents with identical management practices and, at the same time, disconnecting from culturally dissimilar agents. Investigating in detail the statistical mechanics of this model, we find that an increasing rate of information flow through faster imitation dynamics or growing density of network connectivity leads to a marked increase in the likelihood of environmental resource collapse. However, we show that an optimal rate of social network adaptation can mitigate this negative effect without loss of social cohesion through network fragmentation. Our results highlight that seemingly immaterial network dynamics of spreading opinions or values can be of large relevance for the sustainable management of socio-ecological systems and suggest smartly conservative network adaptation as a strategy for mitigating environmental collapse. Hence, facing the great acceleration, these network dynamics should be more routinely incorporated in standard models of economic development or integrated assessment models used for evaluating anthropogenic climate change.

Social integration in friendship networks: The synergy of network structure and peer influence in relation to cigarette smoking among high risk adolescents

PubMed Central

Lakon, Cynthia M.; Valente, Thomas W.

2013-01-01

Using data from a study of high risk adolescents in Southern California, U.S.A. (N = 851), this study examined synergy between social network measures of social integration and peer influence in relation to past month cigarette smoking. Using Hierarchical Linear Modeling, results indicated that being central in networks was significantly and positively related to past month cigarette smoking, across all study models. In addition, there is modest evidence that the number of reciprocated friendship ties was positively related to past month cigarette smoking. There is also some modest evidence that the relationship between having reciprocated friendships and past month cigarette smoking was moderated by a network peer influence process, smoking with those in youths’ best friend networks. Findings indicate that being integrated within a social network context of peer influences favoring drug use relates to more smoking among these high risk youth. PMID:22436575

Incorporating social impact on new product adoption in choice modeing: A case study in green vehicles

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Lin; Wang, Mingxian; Chen, Wei

While discrete choice analysis is prevalent in capturing consumer preferences and describing their choice behaviors in product design, the traditional choice modeling approach assumes that each individual makes independent decisions, without considering the social impact. However, empirical studies show that choice is social - influenced by many factors beyond engineering performance of a product and consumer attributes. To alleviate this limitation, we propose a new choice modeling framework to capture the dynamic influence from social networks on consumer adoption of new products. By introducing social influence attributes into a choice utility function, social network simulation is integrated with the traditionalmore » discrete choice analysis in a three-stage process. Our study shows the need for considering social impact in forecasting new product adoption. Using hybrid electric vehicles as an example, our work illustrates the procedure of social network construction, social influence evaluation, and choice model estimation based on data from the National Household Travel Survey. Our study also demonstrates several interesting findings on the dynamic nature of new technology adoption and how social networks may influence hybrid electric vehicle adoption. (C) 2014 Elsevier Ltd. All rights reserved« less

A Case Study of the Implementation of Social Models of Teaching in E-Learning: "The Social Networks in Education", Online Course of the Inter-Orthodox Centre of the Church of Greece

ERIC Educational Resources Information Center

Komninou, Ioanna

2018-01-01

The development of e-learning has caused a growing interest in learning models that may have the best results. We believe that it is good practice to implement social learning models in the field of online education. In this case, the implementation of complex instruction in online training courses for teachers, on "Social Networks in…

Opinion formation in time-varying social networks: The case of the naming game

NASA Astrophysics Data System (ADS)

Maity, Suman Kalyan; Manoj, T. Venkat; Mukherjee, Animesh

2012-09-01

We study the dynamics of the naming game as an opinion formation model on time-varying social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of time-varying properties of the social network of the agents on the naming game dynamics. In particular, we perform a computational exploration of this model using simulations on top of real networks. We investigate the outcomes of the dynamics on two different types of time-varying data: (1) the networks vary on a day-to-day basis and (2) the networks vary within very short intervals of time (20 sec). In the first case, we find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the naming game in these networks maintains clusters of coexisting opinions indefinitely leading to metastability. In the second case, we investigate the evolution of the naming game in perfect synchronization with the time evolution of the underlying social network shedding new light on the traditional emergent properties of the game that differ largely from what has been reported in the existing literature.

Ties That Bind: A Social Network Approach to Understanding Student Integration and Persistence. ASHE Annual Meeting Paper.

ERIC Educational Resources Information Center

Thomas, Scott L.

This study examined the social networks of college students and how such networks affect student commitment and persistence. The study's theoretical framework was based on application of the social network paradigm to Tinto's Student Integration Model, in which a student's initial commitment is modified over time as a result of the student's…

Intercultural Communication in Online Social Networking Discourse

ERIC Educational Resources Information Center

Chen, Hsin-I

2017-01-01

This article presents a case study that examines how an online social networking community is constituted through intercultural discourse on the part of one learner sojourning in the US. Using Byram's model of intercultural communicative competence, this study examines the learner's naturalistic communication in a social networking site (SNS). The…

Potential of Social Networking Sites for Distance Education Student Engagement

ERIC Educational Resources Information Center

Lester, Jaime; Perini, Michael

2010-01-01

This chapter explores the potential of social networking sites for increasing student engagement for distance education learners. The authors present a modified student engagement model with a focus on the integration of technology, specifically social networking sites for community college distance education learners. The chapter concludes with…

Improving Student Engagement Using Course-Based Social Networks

ERIC Educational Resources Information Center

Imlawi, Jehad Mohammad

2013-01-01

This study proposes an engagement model that supports use of course-based online social networks for engaging student, and hence, improving their educational outcomes. This research demonstrates that instructors who create course-based online social networks to communicate with students can increase the student engagement in these online social…

Exploring Classroom Interaction with Dynamic Social Network Analysis

ERIC Educational Resources Information Center

Bokhove, Christian

2018-01-01

This article reports on an exploratory project in which technology and dynamic social network analysis (SNA) are used for modelling classroom interaction. SNA focuses on the links between social actors, draws on graphic imagery to reveal and display the patterning of those links, and develops mathematical and computational models to describe and…

Energy model for rumor propagation on social networks

NASA Astrophysics Data System (ADS)

Han, Shuo; Zhuang, Fuzhen; He, Qing; Shi, Zhongzhi; Ao, Xiang

2014-01-01

With the development of social networks, the impact of rumor propagation on human lives is more and more significant. Due to the change of propagation mode, traditional rumor propagation models designed for word-of-mouth process may not be suitable for describing the rumor spreading on social networks. To overcome this shortcoming, we carefully analyze the mechanisms of rumor propagation and the topological properties of large-scale social networks, then propose a novel model based on the physical theory. In this model, heat energy calculation formula and Metropolis rule are introduced to formalize this problem and the amount of heat energy is used to measure a rumor’s impact on a network. Finally, we conduct track experiments to show the evolution of rumor propagation, make comparison experiments to contrast the proposed model with the traditional models, and perform simulation experiments to study the dynamics of rumor spreading. The experiments show that (1) the rumor propagation simulated by our model goes through three stages: rapid growth, fluctuant persistence and slow decline; (2) individuals could spread a rumor repeatedly, which leads to the rumor’s resurgence; (3) rumor propagation is greatly influenced by a rumor’s attraction, the initial rumormonger and the sending probability.

Testing a model of facilitated reflection on network feedback: a mixed method study on integration of rural mental healthcare services for older people.

PubMed

Fuller, Jeffrey; Oster, Candice; Muir Cochrane, Eimear; Dawson, Suzanne; Lawn, Sharon; Henderson, Julie; O'Kane, Deb; Gerace, Adam; McPhail, Ruth; Sparkes, Deb; Fuller, Michelle; Reed, Richard L

2015-11-11

To test a management model of facilitated reflection on network feedback as a means to engage services in problem solving the delivery of integrated primary mental healthcare to older people. Participatory mixed methods case study evaluating the impact of a network management model using organisational network feedback (through social network analysis, key informant interviews and policy review). A model of facilitated network reflection using network theory and methods. A rural community in South Australia. 32 staff from 24 services and 12 senior service managers from mental health, primary care and social care services. Health and social care organisations identified that they operated in clustered self-managed networks within sectors, with no overarching purposive older people's mental healthcare network. The model of facilitated reflection revealed service goal and role conflicts. These discussions helped local services to identify as a network, and begin the problem-solving communication and referral links. A Governance Group assisted this process. Barriers to integrated servicing through a network included service funding tied to performance of direct care tasks and the lack of a clear lead network administration organisation. A model of facilitated reflection helped organisations to identify as a network, but revealed sensitivity about organisational roles and goals, which demonstrated that conflict should be expected. Networked servicing needed a neutral network administration organisation with cross-sectoral credibility, a mandate and the resources to monitor the network, to deal with conflict, negotiate commitment among the service managers, and provide opportunities for different sectors to meet and problem solve. This requires consistency and sustained intersectoral policies that include strategies and funding to facilitate and maintain health and social care networks in rural communities. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Effects of Heterogeneous Social Interactions on Flocking Dynamics

NASA Astrophysics Data System (ADS)

Miguel, M. Carmen; Parley, Jack T.; Pastor-Satorras, Romualdo

2018-02-01

Social relationships characterize the interactions that occur within social species and may have an important impact on collective animal motion. Here, we consider a variation of the standard Vicsek model for collective motion in which interactions are mediated by an empirically motivated scale-free topology that represents a heterogeneous pattern of social contacts. We observe that the degree of order of the model is strongly affected by network heterogeneity: more heterogeneous networks show a more resilient ordered state, while less heterogeneity leads to a more fragile ordered state that can be destroyed by sufficient external noise. Our results challenge the previously accepted equivalence between the static Vicsek model and the equilibrium X Y model on the network of connections, and point towards a possible equivalence with models exhibiting a different symmetry.

Social networks and alcohol use disorders: findings from a nationally representative sample

PubMed Central

Mowbray, Orion; Quinn, Adam; Cranford, James A.

2014-01-01

Background While some argue that social network ties of individuals with alcohol use disorders (AUD) are robust, there is evidence to suggest that individuals with AUDs have few social network ties, which are a known risk factor for health and wellness. Objectives Social network ties to friends, family, co-workers and communities of individuals are compared among individuals with a past-year diagnosis of alcohol dependence or alcohol abuse to individuals with no lifetime diagnosis of AUD. Method Respondents from Wave 2 of the National Epidemiologic Survey on Alcohol Related Conditions (NESARC) were assessed for the presence of past-year alcohol dependence or past-year alcohol abuse, social network ties, sociodemographics and clinical characteristics. Results Bivariate analyses showed that both social network size and social network diversity was significantly smaller among individuals with alcohol dependence, compared to individuals with alcohol abuse or no AUD. When social and clinical factors related to AUD status were controlled, multinomial logistic models showed that social network diversity remained a significant predictor of AUD status, while social network size did not differ among AUD groups. Conclusion Social networks of individuals with AUD may be different than individuals with no AUD, but this claim is dependent on specific AUD diagnosis and how social networks are measured. PMID:24405256

Grief responses, coping processes, and social support of widows: research with Roy's model.

PubMed

Robinson, J H

1995-01-01

This ex post facto descriptive correlational design study of widows during their second year of bereavement utilizes Roy's adaptation model as a guiding framework. Contextual stimuli (social support, social network, income/education, spiritual beliefs) were related to the cognator function (coping process), which was related to adaptation outcome (grief response). Significant moderate positive relationships were found between social support and coping process, and between social network and coping process. A significant relationship was also found between coping process and grief response. The path model accounted for 18% explained variance.

Leverage Between the Buffering Effect and the Bystander Effect in Social Networking.

PubMed

Chiu, Yu-Ping; Chang, Shu-Chen

2015-08-01

This study examined encouraged and inhibited social feedback behaviors based on the theories of the buffering effect and the bystander effect. A system program was used to collect personal data and social feedback from a Facebook data set to test the research model. The results revealed that the buffering effect induced a positive relationship between social network size and feedback gained from friends when people's social network size was under a certain cognitive constraint. For people with a social network size that exceeds this cognitive constraint, the bystander effect may occur, in which having more friends may inhibit social feedback. In this study, two social psychological theories were applied to explain social feedback behavior on Facebook, and it was determined that social network size and social feedback exhibited no consistent linear relationship.

A social model based on customers' profiles for analyzing the churning process in the mobile market of data plans

NASA Astrophysics Data System (ADS)

Postigo-Boix, Marcos; Melús-Moreno, José L.

2018-04-01

Mobile Network Operators (MNOs) present wireless services of the same kind in identical zones, clients select the service taking into account any element they consider relevant. Churning hits on the design of the network and the method to assign prices by MNOs, and of course their earnings. Therefore, MNOs try to reduce churn detecting potential churners before they leave the service. Our approach to churn prediction considers each customer individually. Previous research shows that members of the social circle of a subscriber may influence churn. Thus, many scenarios that describe social relations, and in which churning processes could be expected, set an emerging challenge with practical implications. This paper uses the Agent-Based Modeling (ABM) technique to model customers. The model's parameters include demographic and psychographic features as well as usage profiles according to their social behavior considering their customers' profiles. Our model modifies and extends an existing real social network generator algorithm that considers customer's profiles and homophily considerations to create connections. We show that using our approach, groups of customers with greater tendency to churn due to the influence of their social networks can be identified better.

Local Spatial Obesity Analysis and Estimation Using Online Social Network Sensors.

PubMed

Sun, Qindong; Wang, Nan; Li, Shancang; Zhou, Hongyi

2018-03-15

Recently, the online social networks (OSNs) have received considerable attentions as a revolutionary platform to offer users massive social interaction among users that enables users to be more involved in their own healthcare. The OSNs have also promoted increasing interests in the generation of analytical, data models in health informatics. This paper aims at developing an obesity identification, analysis, and estimation model, in which each individual user is regarded as an online social network 'sensor' that can provide valuable health information. The OSN-based obesity analytic model requires each sensor node in an OSN to provide associated features, including dietary habit, physical activity, integral/incidental emotions, and self-consciousness. Based on the detailed measurements on the correlation of obesity and proposed features, the OSN obesity analytic model is able to estimate the obesity rate in certain urban areas and the experimental results demonstrate a high success estimation rate. The measurements and estimation experimental findings created by the proposed obesity analytic model show that the online social networks could be used in analyzing the local spatial obesity problems effectively. Copyright © 2018. Published by Elsevier Inc.

The spreading of opposite opinions on online social networks with authoritative nodes

NASA Astrophysics Data System (ADS)

Yan, Shu; Tang, Shaoting; Pei, Sen; Jiang, Shijin; Zhang, Xiao; Ding, Wenrui; Zheng, Zhiming

2013-09-01

The study of opinion dynamics, such as spreading and controlling of rumors, has become an important issue on social networks. Numerous models have been devised to describe this process, including epidemic models and spin models, which mainly focus on how opinions spread and interact with each other, respectively. In this paper, we propose a model that combines the spreading stage and the interaction stage for opinions to illustrate the process of dispelling a rumor. Moreover, we set up authoritative nodes, which disseminate positive opinion to counterbalance the negative opinion prevailing on online social networking sites. With analysis of the relationship among positive opinion proportion, opinion strength and the density of authoritative nodes in networks with different topologies, we demonstrate that the positive opinion proportion grows with the density of authoritative nodes until the positive opinion prevails in the entire network. In particular, the relationship is linear in homogeneous topologies. Besides, it is also noteworthy that initial locations of the negative opinion source and authoritative nodes do not influence positive opinion proportion in homogeneous networks but have a significant impact on heterogeneous networks. The results are verified by numerical simulations and are helpful to understand the mechanism of two different opinions interacting with each other on online social networking sites.

Coupling ecological and social network models to assess "transmission" and "contagion" of an aquatic invasive species.

PubMed

Haak, Danielle M; Fath, Brian D; Forbes, Valery E; Martin, Dustin R; Pope, Kevin L

2017-04-01

Network analysis is used to address diverse ecological, social, economic, and epidemiological questions, but few efforts have been made to combine these field-specific analyses into interdisciplinary approaches that effectively address how complex systems are interdependent and connected to one another. Identifying and understanding these cross-boundary connections improves natural resource management and promotes proactive, rather than reactive, decisions. This research had two main objectives; first, adapt the framework and approach of infectious disease network modeling so that it may be applied to the socio-ecological problem of spreading aquatic invasive species, and second, use this new coupled model to simulate the spread of the invasive Chinese mystery snail (Bellamya chinensis) in a reservoir network in Southeastern Nebraska, USA. The coupled model integrates an existing social network model of how anglers move on the landscape with new reservoir-specific ecological network models. This approach allowed us to identify 1) how angler movement among reservoirs aids in the spread of B. chinensis, 2) how B. chinensis alters energy flows within individual-reservoir food webs, and 3) a new method for assessing the spread of any number of non-native or invasive species within complex, social-ecological systems. Copyright © 2016 Elsevier Ltd. All rights reserved.

Coupling ecological and social network models to assess “transmission” and “contagion” of an aquatic invasive species

USGS Publications Warehouse

Haak, Danielle M.; Fath, Brian D.; Forbes, Valery E.; Martin, Dustin R.; Pope, Kevin L.

2017-01-01

Network analysis is used to address diverse ecological, social, economic, and epidemiological questions, but few efforts have been made to combine these field-specific analyses into interdisciplinary approaches that effectively address how complex systems are interdependent and connected to one another. Identifying and understanding these cross-boundary connections improves natural resource management and promotes proactive, rather than reactive, decisions. This research had two main objectives; first, adapt the framework and approach of infectious disease network modeling so that it may be applied to the socio-ecological problem of spreading aquatic invasive species, and second, use this new coupled model to simulate the spread of the invasive Chinese mystery snail (Bellamya chinensis) in a reservoir network in Southeastern Nebraska, USA. The coupled model integrates an existing social network model of how anglers move on the landscape with new reservoir-specific ecological network models. This approach allowed us to identify 1) how angler movement among reservoirs aids in the spread of B. chinensis, 2) how B. chinensisalters energy flows within individual-reservoir food webs, and 3) a new method for assessing the spread of any number of non-native or invasive species within complex, social-ecological systems.

Synergistic effects in threshold models on networks.

PubMed

Juul, Jonas S; Porter, Mason A

2018-01-01

Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can-depending on a parameter-either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.

Synergistic effects in threshold models on networks

NASA Astrophysics Data System (ADS)

Juul, Jonas S.; Porter, Mason A.

2018-01-01

Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can—depending on a parameter—either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.

Social Sensor Analytics: Making Sense of Network Models in Social Media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dowling, Chase P.; Harrison, Joshua J.; Sathanur, Arun V.

Social networks can be thought of as noisy sensor networks mapping real world information to the web. Owing to the extensive body of literature in sensor network analysis, this work sought to apply several novel and traditional methods in sensor network analysis for the purposes of efficiently interrogating social media data streams from raw data. We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the months of November 2013more » and June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify forms of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We hope to sufficiently characterize global behavior in a medium such as Twitter as a means of learning global model parameters one may use to predict or simulate behavior on a large scale. We have made our time series and dynamic graph analytical code available via a GitHub repository https://github.com/cpatdowling/salsa and our data are available upon request.« less

Factors which motivate the use of social networks by students.

PubMed

González Sanmamed, Mercedes; Muñoz Carril, Pablo C; Dans Álvarez de Sotomayor, Isabel

2017-05-01

The aim of this research was to identify those factors which motivate the use of social networks by 4th year students in Secondary Education between the ages of 15 and 18. 1,144 students from 29 public and private schools took part. The data were analysed using Partial Least Squares Structural Equation Modelling technique. Versatility was confirmed to be the variable which most influences the motivation of students in their use of social networks. The positive relationship between versatility in the use of social networks and educational uses was also significant. The characteristics of social networks are analysed according to their versatility and how this aspect makes them attractive to students. The positive effects of social networks are discussed in terms of educational uses and their contribution to school learning. There is also a warning about the risks associated with misuse of social networks, and finally, the characteristics and conditions for the development of good educational practice through social networks are identified.

A coevolving model based on preferential triadic closure for social media networks

PubMed Central

Li, Menghui; Zou, Hailin; Guan, Shuguang; Gong, Xiaofeng; Li, Kun; Di, Zengru; Lai, Choy-Heng

2013-01-01

The dynamical origin of complex networks, i.e., the underlying principles governing network evolution, is a crucial issue in network study. In this paper, by carrying out analysis to the temporal data of Flickr and Epinions–two typical social media networks, we found that the dynamical pattern in neighborhood, especially the formation of triadic links, plays a dominant role in the evolution of networks. We thus proposed a coevolving dynamical model for such networks, in which the evolution is only driven by the local dynamics–the preferential triadic closure. Numerical experiments verified that the model can reproduce global properties which are qualitatively consistent with the empirical observations. PMID:23979061

A two-stage broadcast message propagation model in social networks

NASA Astrophysics Data System (ADS)

Wang, Dan; Cheng, Shun-Jun

2016-11-01

Message propagation in social networks is becoming a popular topic in complex networks. One of the message types in social networks is called broadcast message. It refers to a type of message which has a unique and unknown destination for the publisher, such as 'lost and found'. Its propagation always has two stages. Due to this feature, rumor propagation model and epidemic propagation model have difficulty in describing this message's propagation accurately. In this paper, an improved two-stage susceptible-infected-removed model is proposed. We come up with the concept of the first forwarding probability and the second forwarding probability. Another part of our work is figuring out the influence to the successful message transmission chance in each level resulting from multiple reasons, including the topology of the network, the receiving probability, the first stage forwarding probability, the second stage forwarding probability as well as the length of the shortest path between the publisher and the relevant destination. The proposed model has been simulated on real networks and the results proved the model's effectiveness.

Social networking policies in nursing education.

PubMed

Frazier, Blake; Culley, Joan M; Hein, Laura C; Williams, Amber; Tavakoli, Abbas S

2014-03-01

Social networking use has increased exponentially in the past few years. A literature review related to social networking and nursing revealed a research gap between nursing practice and education. Although there was information available on the appropriate use of social networking sites, there was limited research on the use of social networking policies within nursing education. The purpose of this study was to identify current use of social media by faculty and students and a need for policies within nursing education at one institution. A survey was developed and administered to nursing students (n = 273) and nursing faculty (n = 33). Inferential statistics included χ², Fisher exact test, t test, and General Linear Model. Cronbach's α was used to assess internal consistency of social media scales. The χ² result indicates that there were associations with the group and several social media items. t Test results indicate significant differences between student and faculty for average of policies are good (P = .0127), policies and discipline (P = .0315), and policy at the study school (P = .0013). General Linear Model analyses revealed significant differences for "friend" a patient with a bond, unprofessional posts, policy, and nursing with class level. Results showed that students and faculty supported the development of a social networking policy.

Dynamics of deceptive interactions in social networks.

PubMed

Barrio, Rafael A; Govezensky, Tzipe; Dunbar, Robin; Iñiguez, Gerardo; Kaski, Kimmo

2015-11-06

In this paper, we examine the role of lies in human social relations by implementing some salient characteristics of deceptive interactions into an opinion formation model, so as to describe the dynamical behaviour of a social network more realistically. In this model, we take into account such basic properties of social networks as the dynamics of the intensity of interactions, the influence of public opinion and the fact that in every human interaction it might be convenient to deceive or withhold information depending on the instantaneous situation of each individual in the network. We find that lies shape the topology of social networks, especially the formation of tightly linked, small communities with loose connections between them. We also find that agents with a larger proportion of deceptive interactions are the ones that connect communities of different opinion, and, in this sense, they have substantial centrality in the network. We then discuss the consequences of these results for the social behaviour of humans and predict the changes that could arise due to a varying tolerance for lies in society. © 2015 The Author(s).

A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services.

PubMed

Fujiyama, Toshifumi; Matsui, Chihiro; Takemura, Akimichi

2016-01-01

We propose a power-law growth and decay model for posting data to social networking services before and after social events. We model the time series structure of deviations from the power-law growth and decay with a conditional Poisson autoregressive (AR) model. Online postings related to social events are described by five parameters in the power-law growth and decay model, each of which characterizes different aspects of interest in the event. We assess the validity of parameter estimates in terms of confidence intervals, and compare various submodels based on likelihoods and information criteria.

Social networks and trade of services: modelling interregional flows with spatial and network autocorrelation effects

NASA Astrophysics Data System (ADS)

de la Mata, Tamara; Llano, Carlos

2013-07-01

Recent literature on border effect has fostered research on informal barriers to trade and the role played by network dependencies. In relation to social networks, it has been shown that intensity of trade in goods is positively correlated with migration flows between pairs of countries/regions. In this article, we investigate whether such a relation also holds for interregional trade of services. We also consider whether interregional trade flows in services linked with tourism exhibit spatial and/or social network dependence. Conventional empirical gravity models assume the magnitude of bilateral flows between regions is independent of flows to/from regions located nearby in space, or flows to/from regions related through social/cultural/ethic network connections. With this aim, we provide estimates from a set of gravity models showing evidence of statistically significant spatial and network (demographic) dependence in the bilateral flows of the trade of services considered. The analysis has been applied to the Spanish intra- and interregional monetary flows of services from the accommodation, restaurants and travel agencies for the period 2000-2009, using alternative datasets for the migration stocks and definitions of network effects.

Burstiness and tie activation strategies in time-varying social networks.

PubMed

Ubaldi, Enrico; Vezzani, Alessandro; Karsai, Márton; Perra, Nicola; Burioni, Raffaella

2017-04-13

The recent developments in the field of social networks shifted the focus from static to dynamical representations, calling for new methods for their analysis and modelling. Observations in real social systems identified two main mechanisms that play a primary role in networks' evolution and influence ongoing spreading processes: the strategies individuals adopt when selecting between new or old social ties, and the bursty nature of the social activity setting the pace of these choices. We introduce a time-varying network model accounting both for ties selection and burstiness and we analytically study its phase diagram. The interplay of the two effects is non trivial and, interestingly, the effects of burstiness might be suppressed in regimes where individuals exhibit a strong preference towards previously activated ties. The results are tested against numerical simulations and compared with two empirical datasets with very good agreement. Consequently, the framework provides a principled method to classify the temporal features of real networks, and thus yields new insights to elucidate the effects of social dynamics on spreading processes.

Opinion evolution in different social acquaintance networks.

PubMed

Chen, Xi; Zhang, Xiao; Wu, Zhan; Wang, Hongwei; Wang, Guohua; Li, Wei

2017-11-01

Social acquaintance networks influenced by social culture and social policy have a great impact on public opinion evolution in daily life. Based on the differences between socio-culture and social policy, three different social acquaintance networks (kinship-priority acquaintance network, independence-priority acquaintance network, and hybrid acquaintance network) incorporating heredity proportion p h and variation proportion p v are proposed in this paper. Numerical experiments are conducted to investigate network topology and different phenomena during opinion evolution, using the Deffuant model. We found that in kinship-priority acquaintance networks, similar to the Chinese traditional acquaintance networks, opinions always achieve fragmentation, resulting in the formation of multiple large clusters and many small clusters due to the fact that individuals believe more in their relatives and live in a relatively closed environment. In independence-priority acquaintance networks, similar to Western acquaintance networks, the results are similar to those in the kinship-priority acquaintance network. In hybrid acquaintance networks, similar to the Chinese modern acquaintance networks, only a few clusters are formed indicating that in modern China, opinions are more likely to reach consensus on a large scale. These results are similar to the opinion evolution phenomena in modern society, proving the rationality and applicability of network models combined with social culture and policy. We also found a threshold curve p v +2p h =2.05 in the results for the final opinion clusters and evolution time. Above the threshold curve, opinions could easily reach consensus. Based on the above experimental results, a culture-policy-driven mechanism for the opinion dynamic is worth promoting in this paper, that is, opinion dynamics can be driven by different social cultures and policies through the influence of heredity and variation in interpersonal relationship networks. This finding is of great significance for predicting opinion evolution under different acquaintance networks and formulating reasonable policies based on cultural characteristics to guide public opinion.

Opinion evolution in different social acquaintance networks

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhang, Xiao; Wu, Zhan; Wang, Hongwei; Wang, Guohua; Li, Wei

2017-11-01

Social acquaintance networks influenced by social culture and social policy have a great impact on public opinion evolution in daily life. Based on the differences between socio-culture and social policy, three different social acquaintance networks (kinship-priority acquaintance network, independence-priority acquaintance network, and hybrid acquaintance network) incorporating heredity proportion ph and variation proportion pv are proposed in this paper. Numerical experiments are conducted to investigate network topology and different phenomena during opinion evolution, using the Deffuant model. We found that in kinship-priority acquaintance networks, similar to the Chinese traditional acquaintance networks, opinions always achieve fragmentation, resulting in the formation of multiple large clusters and many small clusters due to the fact that individuals believe more in their relatives and live in a relatively closed environment. In independence-priority acquaintance networks, similar to Western acquaintance networks, the results are similar to those in the kinship-priority acquaintance network. In hybrid acquaintance networks, similar to the Chinese modern acquaintance networks, only a few clusters are formed indicating that in modern China, opinions are more likely to reach consensus on a large scale. These results are similar to the opinion evolution phenomena in modern society, proving the rationality and applicability of network models combined with social culture and policy. We also found a threshold curve pv+2 ph=2.05 in the results for the final opinion clusters and evolution time. Above the threshold curve, opinions could easily reach consensus. Based on the above experimental results, a culture-policy-driven mechanism for the opinion dynamic is worth promoting in this paper, that is, opinion dynamics can be driven by different social cultures and policies through the influence of heredity and variation in interpersonal relationship networks. This finding is of great significance for predicting opinion evolution under different acquaintance networks and formulating reasonable policies based on cultural characteristics to guide public opinion.

Spreading gossip in social networks.

PubMed

Lind, Pedro G; da Silva, Luciano R; Andrade, José S; Herrmann, Hans J

2007-09-01

We study a simple model of information propagation in social networks, where two quantities are introduced: the spread factor, which measures the average maximal reachability of the neighbors of a given node that interchange information among each other, and the spreading time needed for the information to reach such a fraction of nodes. When the information refers to a particular node at which both quantities are measured, the model can be taken as a model for gossip propagation. In this context, we apply the model to real empirical networks of social acquaintances and compare the underlying spreading dynamics with different types of scale-free and small-world networks. We find that the number of friendship connections strongly influences the probability of being gossiped. Finally, we discuss how the spread factor is able to be applied to other situations.

Spreading gossip in social networks

NASA Astrophysics Data System (ADS)

Lind, Pedro G.; da Silva, Luciano R.; Andrade, José S., Jr.; Herrmann, Hans J.

2007-09-01

We study a simple model of information propagation in social networks, where two quantities are introduced: the spread factor, which measures the average maximal reachability of the neighbors of a given node that interchange information among each other, and the spreading time needed for the information to reach such a fraction of nodes. When the information refers to a particular node at which both quantities are measured, the model can be taken as a model for gossip propagation. In this context, we apply the model to real empirical networks of social acquaintances and compare the underlying spreading dynamics with different types of scale-free and small-world networks. We find that the number of friendship connections strongly influences the probability of being gossiped. Finally, we discuss how the spread factor is able to be applied to other situations.

How events determine spreading patterns: information transmission via internal and external influences on social networks

NASA Astrophysics Data System (ADS)

Liu, Chuang; Zhan, Xiu-Xiu; Zhang, Zi-Ke; Sun, Gui-Quan; Hui, Pak Ming

2015-11-01

Recently, information transmission models motivated by the classical epidemic propagation, have been applied to a wide-range of social systems, generally assume that information mainly transmits among individuals via peer-to-peer interactions on social networks. In this paper, we consider one more approach for users to get information: the out-of-social-network influence. Empirical analyzes of eight typical events’ diffusion on a very large micro-blogging system, Sina Weibo, show that the external influence has significant impact on information spreading along with social activities. In addition, we propose a theoretical model to interpret the spreading process via both internal and external channels, considering three essential properties: (i) memory effect; (ii) role of spreaders; and (iii) non-redundancy of contacts. Experimental and mathematical results indicate that the information indeed spreads much quicker and broader with mutual effects of the internal and external influences. More importantly, the present model reveals that the event characteristic would highly determine the essential spreading patterns once the network structure is established. The results may shed some light on the in-depth understanding of the underlying dynamics of information transmission on real social networks.

Agent-Based Modeling of China's Rural-Urban Migration and Social Network Structure.

PubMed

Fu, Zhaohao; Hao, Lingxin

2018-01-15

We analyze China's rural-urban migration and endogenous social network structures using agent-based modeling. The agents from census micro data are located in their rural origin with an empirical-estimated prior propensity to move. The population-scale social network is a hybrid one, combining observed family ties and locations of the origin with a parameter space calibrated from census, survey and aggregate data and sampled using a stepwise Latin Hypercube Sampling method. At monthly intervals, some agents migrate and these migratory acts change the social network by turning within-nonmigrant connections to between-migrant-nonmigrant connections, turning local connections to nonlocal connections, and adding among-migrant connections. In turn, the changing social network structure updates migratory propensities of those well-connected nonmigrants who become more likely to move. These two processes iterate over time. Using a core-periphery method developed from the k -core decomposition method, we identify and quantify the network structural changes and map these changes with the migration acceleration patterns. We conclude that network structural changes are essential for explaining migration acceleration observed in China during the 1995-2000 period.

Agent-based modeling of China's rural-urban migration and social network structure

NASA Astrophysics Data System (ADS)

Fu, Zhaohao; Hao, Lingxin

2018-01-01

We analyze China's rural-urban migration and endogenous social network structures using agent-based modeling. The agents from census micro data are located in their rural origin with an empirical-estimated prior propensity to move. The population-scale social network is a hybrid one, combining observed family ties and locations of the origin with a parameter space calibrated from census, survey and aggregate data and sampled using a stepwise Latin Hypercube Sampling method. At monthly intervals, some agents migrate and these migratory acts change the social network by turning within-nonmigrant connections to between-migrant-nonmigrant connections, turning local connections to nonlocal connections, and adding among-migrant connections. In turn, the changing social network structure updates migratory propensities of those well-connected nonmigrants who become more likely to move. These two processes iterate over time. Using a core-periphery method developed from the k-core decomposition method, we identify and quantify the network structural changes and map these changes with the migration acceleration patterns. We conclude that network structural changes are essential for explaining migration acceleration observed in China during the 1995-2000 period.

Competing opinion diffusion on social networks.

PubMed

Hu, Haibo

2017-11-01

Opinion competition is a common phenomenon in real life, such as with opinions on controversial issues or political candidates; however, modelling this competition remains largely unexplored. To bridge this gap, we propose a model of competing opinion diffusion on social networks taking into account degree-dependent fitness or persuasiveness. We study the combined influence of social networks, individual fitnesses and attributes, as well as mass media on people's opinions, and find that both social networks and mass media act as amplifiers in opinion diffusion, the amplifying effect of which can be quantitatively characterized. We analytically obtain the probability that each opinion will ultimately pervade the whole society when there are no committed people in networks, and the final proportion of each opinion at the steady state when there are committed people in networks. The results of numerical simulations show good agreement with those obtained through an analytical approach. This study provides insight into the collective influence of individual attributes, local social networks and global media on opinion diffusion, and contributes to a comprehensive understanding of competing diffusion behaviours in the real world.

Competing opinion diffusion on social networks

PubMed Central

2017-01-01

Opinion competition is a common phenomenon in real life, such as with opinions on controversial issues or political candidates; however, modelling this competition remains largely unexplored. To bridge this gap, we propose a model of competing opinion diffusion on social networks taking into account degree-dependent fitness or persuasiveness. We study the combined influence of social networks, individual fitnesses and attributes, as well as mass media on people’s opinions, and find that both social networks and mass media act as amplifiers in opinion diffusion, the amplifying effect of which can be quantitatively characterized. We analytically obtain the probability that each opinion will ultimately pervade the whole society when there are no committed people in networks, and the final proportion of each opinion at the steady state when there are committed people in networks. The results of numerical simulations show good agreement with those obtained through an analytical approach. This study provides insight into the collective influence of individual attributes, local social networks and global media on opinion diffusion, and contributes to a comprehensive understanding of competing diffusion behaviours in the real world. PMID:29291101

Adaptive Network Dynamics - Modeling and Control of Time-Dependent Social Contacts

PubMed Central

Schwartz, Ira B.; Shaw, Leah B.; Shkarayev, Maxim S.

2013-01-01

Real networks consisting of social contacts do not possess static connections. That is, social connections may be time dependent due to a variety of individual behavioral decisions based on current network connections. Examples of adaptive networks occur in epidemics, where information about infectious individuals may change the rewiring of healthy people, or in the recruitment of individuals to a cause or fad, where rewiring may optimize recruitment of susceptible individuals. In this paper, we will review some of the dynamical properties of adaptive networks, and show how they predict novel phenomena as well as yield insight into new controls. The applications will be control of epidemic outbreaks and terrorist recruitment modeling. PMID:25414913

MSEE: Stochastic Cognitive Linguistic Behavior Models for Semantic Sensing

DTIC Science & Technology

2013-09-01

recognition, a Gaussian Process Dynamic Model with Social Network Analysis (GPDM-SNA) for a small human group action recognition, an extended GPDM-SNA...44  3.2. Small Human Group Activity Modeling Based on Gaussian Process Dynamic Model and Social Network Analysis (SN-GPDM...51  Approved for public release; distribution unlimited. 3 3.2.3. Gaussian Process Dynamical Model and

Simulating drinking in social networks to inform alcohol prevention and treatment efforts.

PubMed

Hallgren, Kevin A; McCrady, Barbara S; Caudell, Thomas P; Witkiewitz, Katie; Tonigan, J Scott

2017-11-01

Adolescent drinking influences, and is influenced by, peer alcohol use. Several efficacious adolescent alcohol interventions include elements aimed at reducing susceptibility to peer influence. Modeling these interventions within dynamically changing social networks may improve our understanding of how such interventions work and for whom they work best. We used stochastic actor-based models to simulate longitudinal drinking and friendship formation within social networks using parameters obtained from a meta-analysis of real-world 10th grade adolescent social networks. Levels of social influence (i.e., friends affecting changes in one's drinking) and social selection (i.e., drinking affecting changes in one's friendships) were manipulated at several levels, which directly impacted the degree of clustering in friendships based on similarity in drinking behavior. Midway through each simulation, one randomly selected heavy-drinking actor from each network received an "intervention" that either (a) reduced their susceptibility to social influence, (b) reduced their susceptibility to social selection, (c) eliminated a friendship with a heavy drinker, or (d) initiated a friendship with a nondrinker. Only the intervention that eliminated targeted actors' susceptibility to social influence consistently reduced that actor's drinking. Moreover, this was only effective in networks with social influence and social selection that were at higher levels than what was found in the real-world reference study. Social influence and social selection are dynamic processes that can lead to complex systems that may moderate the effectiveness of network-based interventions. Interventions that reduce susceptibility to social influence may be most effective among adolescents with high susceptibility to social influence and heavier-drinking friends. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Ontology Design of Influential People Identification Using Centrality

NASA Astrophysics Data System (ADS)

Maulana Awangga, Rolly; Yusril, Muhammad; Setyawan, Helmi

2018-04-01

Identifying influential people as a node in a graph theory commonly calculated by social network analysis. The social network data has the user as node and edge as relation forming a friend relation graph. This research is conducting different meaning of every nodes relation in the social network. Ontology was perfect match science to describe the social network data as conceptual and domain. Ontology gives essential relationship in a social network more than a current graph. Ontology proposed as a standard for knowledge representation for the semantic web by World Wide Web Consortium. The formal data representation use Resource Description Framework (RDF) and Web Ontology Language (OWL) which is strategic for Open Knowledge-Based website data. Ontology used in the semantic description for a relationship in the social network, it is open to developing semantic based relationship ontology by adding and modifying various and different relationship to have influential people as a conclusion. This research proposes a model using OWL and RDF for influential people identification in the social network. The study use degree centrality, between ness centrality, and closeness centrality measurement for data validation. As a conclusion, influential people identification in Facebook can use proposed Ontology model in the Group, Photos, Photo Tag, Friends, Events and Works data.

Understanding how social networking influences perceived satisfaction with conference experiences

USGS Publications Warehouse

van Riper, Carena J.; van Riper, Charles; Kyle, Gerard T.; Lee, Martha E.

2013-01-01

Social networking is a key benefit derived from participation in conferences that bind the ties of a professional community. Building social networks can lead to satisfactory experiences while furthering participants' long- and short-term career goals. Although investigations of social networking can lend insight into how to effectively engage individuals and groups within a professional cohort, this area has been largely overlooked in past research. The present study investigates the relationship between social networking and satisfaction with the 10th Biennial Conference of Research on the Colorado Plateau using structural equation modelling. Results partially support the hypothesis that three dimensions of social networking – interpersonal connections, social cohesion, and secondary associations – positively contribute to the performance of various conference attributes identified in two focus group sessions. The theoretical and applied contributions of this paper shed light on the social systems formed within professional communities and resource allocation among service providers.

Empirical Models of Social Learning in a Large, Evolving Network.

PubMed

Bener, Ayşe Başar; Çağlayan, Bora; Henry, Adam Douglas; Prałat, Paweł

2016-01-01

This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals' access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends.

Empirical Models of Social Learning in a Large, Evolving Network

PubMed Central

Bener, Ayşe Başar; Çağlayan, Bora; Henry, Adam Douglas; Prałat, Paweł

2016-01-01

This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals’ access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends. PMID:27701430

Sustainability and collapse in a coevolutionary model of local resource stocks and behavioral patterns on a social network

NASA Astrophysics Data System (ADS)

Wiedermann, Marc; Donges, Jonathan F.; Heitzig, Jobst; Kurths, Jürgen

2014-05-01

When investigating the causes and consequences of global change, the collective behavior of human beings is considered as having a considerable impact on natural systems. In our work, we propose a conceptual coevolutionary model simulating the dynamics of local renewable resources in interaction with simplistic societal agents exploiting those resources. The society is represented by a social network on which social traits may be transmitted between agents. These traits themselves induce a certain rate of exploitation of the resource, leading either to its depletion or sustainable existence. Traits are exchanged probabilistically according to their instantaneous individual payoff, and hence this process depends on the status of the natural resource. At the same time agents may adaptively restructure their set of acquaintances. Connections with agents having a different trait may be broken while new connections with agents of the same trait are established. We investigate which choices of social parameters, like the frequency of social interaction, rationality and rate of social network adaptation, cause the system to end in a sustainable state and, hence, what can be done to avoid a collapse of the entire system. The importance and influence of the social network structure is analyzed by the variation of link-densities in the underlying network topology and shows significant influence on the expected outcome of the model. For a static network with no adaptation we find a robust phase transition between the two different regimes, sustainable and non-sustainable, which co-exist in parameter space. High connectivity within the social network, e.g., high link-densities, in combination with a fast rate of social learning lead to a likely collapse of the entire co-evolutionary system, whereas slow learning and small network connectivity very likely result in the sustainable existence of the natural resources. Collapse may be avoided by an intelligent rewiring, e.g. adaptation, of the social network that may also lead to the isolation of misbehaving parts of the society. Our results may suggest that with the current trend to faster imitation and ever increasing global network connectivity, societies are becoming more vulnerable to environmental collapse if they remain myopic at the same time.

Evolution of individual versus social learning on social networks.

PubMed

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-03-06

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of 'cultural models' exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

The social brain: scale-invariant layering of Erdős-Rényi networks in small-scale human societies.

PubMed

Harré, Michael S; Prokopenko, Mikhail

2016-05-01

The cognitive ability to form social links that can bind individuals together into large cooperative groups for safety and resource sharing was a key development in human evolutionary and social history. The 'social brain hypothesis' argues that the size of these social groups is based on a neurologically constrained capacity for maintaining long-term stable relationships. No model to date has been able to combine a specific socio-cognitive mechanism with the discrete scale invariance observed in ethnographic studies. We show that these properties result in nested layers of self-organizing Erdős-Rényi networks formed by each individual's ability to maintain only a small number of social links. Each set of links plays a specific role in the formation of different social groups. The scale invariance in our model is distinct from previous 'scale-free networks' studied using much larger social groups; here, the scale invariance is in the relationship between group sizes, rather than in the link degree distribution. We also compare our model with a dominance-based hierarchy and conclude that humans were probably egalitarian in hunter-gatherer-like societies, maintaining an average maximum of four or five social links connecting all members in a largest social network of around 132 people. © 2016 The Author(s).

A national study predicting licensed social workers' levels of political participation: the role of resources, psychological engagement, and recruitment networks.

PubMed

Ritter, Jessica A

2008-10-01

The social work literature is replete with studies evaluating social workers' direct practice interventions, but strikingly few have assessed how well social workers are faring in the political arena. This study tests a major theoretical model, the civic voluntarism model, developed to explain why some citizens become involved in politics, whereas others do not. The study sample consisted of 396 randomly selected social workers licensed in 11 states, all of whom completed a 25-minute telephone survey. Social workers were surveyed to determine the role of the following variables in explaining social workers' political activity levels-resources needed to participate, psychological engagement, and attachment to recruitment networks. The results indicate that the civic voluntarism model was significant and accounted for 42 percent of the variance. The strongest predictors of social workers' political activity were NASW membership and political interest. This study provides empirical support for the idea that being connected to social networks and having a psychological engagement with politics are crucial factors in explaining social workers' political participation. Implications for social work education are included.

Brain and Social Networks: Fundamental Building Blocks of Human Experience.

PubMed

Falk, Emily B; Bassett, Danielle S

2017-09-01

How do brains shape social networks, and how do social ties shape the brain? Social networks are complex webs by which ideas spread among people. Brains comprise webs by which information is processed and transmitted among neural units. While brain activity and structure offer biological mechanisms for human behaviors, social networks offer external inducers or modulators of those behaviors. Together, these two axes represent fundamental contributors to human experience. Integrating foundational knowledge from social and developmental psychology and sociology on how individuals function within dyads, groups, and societies with recent advances in network neuroscience can offer new insights into both domains. Here, we use the example of how ideas and behaviors spread to illustrate the potential of multilayer network models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Social Networks and Welfare in Future Animal Management.

PubMed

Koene, Paul; Ipema, Bert

2014-03-17

It may become advantageous to keep human-managed animals in the social network groups to which they have adapted. Data concerning the social networks of farm animal species and their ancestors are scarce but essential to establishing the importance of a natural social network for farmed animal species. Social Network Analysis (SNA) facilitates the characterization of social networking at group, subgroup and individual levels. SNA is currently used for modeling the social behavior and management of wild animals and social welfare of zoo animals. It has been recognized for use with farm animals but has yet to be applied for management purposes. Currently, the main focus is on cattle, because in large groups (poultry), recording of individuals is expensive and the existence of social networks is uncertain due to on-farm restrictions. However, in many cases, a stable social network might be important to individual animal fitness, survival and welfare. For instance, when laying hens are not too densely housed, simple networks may be established. We describe here small social networks in horses, brown bears, laying hens and veal calves to illustrate the importance of measuring social networks among animals managed by humans. Emphasis is placed on the automatic measurement of identity, location, nearest neighbors and nearest neighbor distance for management purposes. It is concluded that social networks are important to the welfare of human-managed animal species and that welfare management based on automatic recordings will become available in the near future.

Actor and partner effects of perceived HIV stigma on social network components among people living with HIV/AIDS and their caregivers

PubMed Central

Hao, Chun; Liu, Hongjie

2014-01-01

Background Few studies have investigated the relationship between HIV stigma and social network components at the dyadic level. The objective of this study was to examine the actor and partner effects of perceived HIV stigma by people living with HIV/AIDS (PLWHAs) and their caregivers on social network variables at the dyadic level. Method An egocentric social network study was conducted among 147 dyads consisting of one PLWHA and one caregiver (294 participants) in Nanning, China. The actor-partner interdependence model (APIM) was used to analyze the relationships between perceived HIV stigma and social network components (network relations, network structures, and network functions) at the dyadic level. Results We found in this dyadic analysis that: (1) social network components were similar between PLWHAs and their caregivers; (2) HIV stigma perceived by PLWHAs influenced their own social network components, whereas this influence did not exist between caregivers' perceived HIV stigma and their own social network components; (3) a few significant partner effects were observed between HIV stigma and social network components among both PLWHAs and caregivers. Conclusion The interrelationships between HIV stigma and social network components were complex at the dyadic level. Future interventions programs targeting HIV stigma should focus on the interpersonal relationship at the dyadic level, beyond the intrapersonal factors. PMID:25085478

Actor and partner effects of perceived HIV stigma on social network components among people living with HIV/AIDS and their caregivers.

PubMed

Hao, Chun; Liu, Hongjie

2015-06-01

Few studies have investigated the relationship between HIV stigma and social network components at the dyadic level. The objective of this study was to examine the actor and partner effects of perceived HIV stigma by people living with HIV/AIDS (PLWHAs) and their caregivers on social network variables at the dyadic level. An egocentric social network study was conducted among 147 dyads consisting of one PLWHA and one caregiver (294 participants) in Nanning, China. The actor-partner interdependence model (APIM) was used to analyze the relationships between perceived HIV stigma and social network components (network relations, network structures, and network functions) at the dyadic level. We found in this dyadic analysis that: (1) social network components were similar between PLWHAs and their caregivers; (2) HIV stigma perceived by PLWHAs influenced their own social network components, whereas this influence did not exist between caregivers' perceived HIV stigma and their own social network components; (3) a few significant partner effects were observed between HIV stigma and social network components among both PLWHAs and caregivers. The interrelationships between HIV stigma and social network components were complex at the dyadic level. Future interventions programs targeting HIV stigma should focus on the interpersonal relationship at the dyadic level, beyond the intrapersonal factors. © The Author(s) 2014.

Sampling from complex networks using distributed learning automata

NASA Astrophysics Data System (ADS)

Rezvanian, Alireza; Rahmati, Mohammad; Meybodi, Mohammad Reza

2014-02-01

A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due to a growing trend in analysis of online social networks as dynamic complex large-scale graphs. Due to the large scale and limited access of real networks, the network model is characterized using an appropriate part of a network by sampling approaches. In this paper, a new sampling algorithm based on distributed learning automata has been proposed for sampling from complex networks. In the proposed algorithm, a set of distributed learning automata cooperate with each other in order to take appropriate samples from the given network. To investigate the performance of the proposed algorithm, several simulation experiments are conducted on well-known complex networks. Experimental results are compared with several sampling methods in terms of different measures. The experimental results demonstrate the superiority of the proposed algorithm over the others.

Modeling socio-cultural processes in network-centric environments

NASA Astrophysics Data System (ADS)

Santos, Eunice E.; Santos, Eugene, Jr.; Korah, John; George, Riya; Gu, Qi; Kim, Keumjoo; Li, Deqing; Russell, Jacob; Subramanian, Suresh

2012-05-01

The major focus in the field of modeling & simulation for network centric environments has been on the physical layer while making simplifications for the human-in-the-loop. However, the human element has a big impact on the capabilities of network centric systems. Taking into account the socio-behavioral aspects of processes such as team building, group decision-making, etc. are critical to realistically modeling and analyzing system performance. Modeling socio-cultural processes is a challenge because of the complexity of the networks, dynamism in the physical and social layers, feedback loops and uncertainty in the modeling data. We propose an overarching framework to represent, model and analyze various socio-cultural processes within network centric environments. The key innovation in our methodology is to simultaneously model the dynamism in both the physical and social layers while providing functional mappings between them. We represent socio-cultural information such as friendships, professional relationships and temperament by leveraging the Culturally Infused Social Network (CISN) framework. The notion of intent is used to relate the underlying socio-cultural factors to observed behavior. We will model intent using Bayesian Knowledge Bases (BKBs), a probabilistic reasoning network, which can represent incomplete and uncertain socio-cultural information. We will leverage previous work on a network performance modeling framework called Network-Centric Operations Performance and Prediction (N-COPP) to incorporate dynamism in various aspects of the physical layer such as node mobility, transmission parameters, etc. We validate our framework by simulating a suitable scenario, incorporating relevant factors and providing analyses of the results.

Improving Family Forest Knowledge Transfer through Social Network Analysis

ERIC Educational Resources Information Center

Gorczyca, Erika L.; Lyons, Patrick W.; Leahy, Jessica E.; Johnson, Teresa R.; Straub, Crista L.

2012-01-01

To better engage Maine's family forest landowners our study used social network analysis: a computational social science method for identifying stakeholders, evaluating models of engagement, and targeting areas for enhanced partnerships. Interviews with researchers associated with a research center were conducted to identify how social network…

Why Do You Adopt Social Networking Sites? Investigating the Driving Factors through Structural Equation Modelling

ERIC Educational Resources Information Center

Jan, Muhammad Tahir

2017-01-01

Purpose: The purpose of this paper is to investigate those factors that are associated with the adoption of social networking sites from the perspective of Muslim users residing in Malaysia. Design/methodology/approach: A complete self-administered questionnaire was collected from 223 Muslim users of social networking sites in Malaysia. Both…

Infering and Calibrating Triadic Closure in a Dynamic Network

NASA Astrophysics Data System (ADS)

Mantzaris, Alexander V.; Higham, Desmond J.

In the social sciences, the hypothesis of triadic closure contends that new links in a social contact network arise preferentially between those who currently share neighbours. Here, in a proof-of-principle study, we show how to calibrate a recently proposed evolving network model to time-dependent connectivity data. The probabilistic edge birth rate in the model contains a triadic closure term, so we are also able to assess statistically the evidence for this effect. The approach is shown to work on data generated synthetically from the model. We then apply this methodology to some real, large-scale data that records the build up of connections in a business-related social networking site, and find evidence for triadic closure.

Know the Network, Knit the Network: Applying SNA to N2C2 Maturity Model Experiments

DTIC Science & Technology

2010-06-01

Networks (COINS) 2009. Procedia - Social and Behavioral Sciences (2009). Snijders, Tom A.B., Christian E. G. Steglich and Michael Schweinberger...8217 patterning that create social structures. As an interdisciplinary behavioural science specialty, SNA defends that social actors are interdependent...production of social science data involve a process of interpretation. To carry out such interpretation robustly it is understood that it is imperative to

A cooperative game framework for detecting overlapping communities in social networks

NASA Astrophysics Data System (ADS)

Jonnalagadda, Annapurna; Kuppusamy, Lakshmanan

2018-02-01

Community detection in social networks is a challenging and complex task, which received much attention from researchers of multiple domains in recent years. The evolution of communities in social networks happens merely due to the self-interest of the nodes. The interesting feature of community structure in social networks is the multi membership of the nodes resulting in overlapping communities. Assuming the nodes of the social network as self-interested players, the dynamics of community formation can be captured in the form of a game. In this paper, we propose a greedy algorithm, namely, Weighted Graph Community Game (WGCG), in order to model the interactions among the self-interested nodes of the social network. The proposed algorithm employs the Shapley value mechanism to discover the inherent communities of the underlying social network. The experimental evaluation on the real-world and synthetic benchmark networks demonstrates that the performance of the proposed algorithm is superior to the state-of-the-art overlapping community detection algorithms.

Digital Social Network Mining for Topic Discovery

NASA Astrophysics Data System (ADS)

Moradianzadeh, Pooya; Mohi, Maryam; Sadighi Moshkenani, Mohsen

Networked computers are expanding more and more around the world, and digital social networks becoming of great importance for many people's work and leisure. This paper mainly focused on discovering the topic of exchanging information in digital social network. In brief, our method is to use a hierarchical dictionary of related topics and words that mapped to a graph. Then, with comparing the extracted keywords from the context of social network with graph nodes, probability of relation between context and desired topics will be computed. This model can be used in many applications such as advertising, viral marketing and high-risk group detection.

Friend suggestion in social network based on user log

NASA Astrophysics Data System (ADS)

Kaviya, R.; Vanitha, M.; Sumaiya Thaseen, I.; Mangaiyarkarasi, R.

2017-11-01

Simple friend recommendation algorithms such as similarity, popularity and social aspects is the basic requirement to be explored to methodically form high-performance social friend recommendation. Suggestion of friends is followed. No tags of character were followed. In the proposed system, we use an algorithm for network correlation-based social friend recommendation (NC-based SFR).It includes user activities like where one lives and works. A new friend recommendation method, based on network correlation, by considering the effect of different social roles. To model the correlation between different networks, we develop a method that aligns these networks through important feature selection. We consider by preserving the network structure for a more better recommendations so that it significantly improves the accuracy for better friend-recommendation.

Social support networks and depression of women suffering from early-stage breast cancer: a case control study.

PubMed

Gagliardi, Cristina; Vespa, Anna; Papa, Roberta; Mariotti, Carlo; Cascinu, Stefano; Rossini, Simonetta

2009-01-01

The aim of this study was to investigate the areas of depression, anxiety, and social support using the structural model of the social network. By comparing the networks of two samples of breast cancer sufferers and healthy control participants, it was possible to identify differences in their relationships, in the shape of the networks themselves, and in the levels of depression and anxiety. Women with breast cancer described smaller and denser networks, including mainly kins whereas the healthy women included more friends, coworkers, and leisure companions. The levels of anxiety and depression were higher in women with breast cancer. Social network and social support measure correlated differently with depression and anxiety in the two groups.

Unraveling the disease consequences and mechanisms of modular structure in animal social networks

PubMed Central

Leu, Stephan T.; Cross, Paul C.; Hudson, Peter J.; Bansal, Shweta

2017-01-01

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living. PMID:28373567

Unraveling the disease consequences and mechanisms of modular structure in animal social networks

USGS Publications Warehouse

Sah, Pratha; Leu, Stephan T.; Cross, Paul C.; Hudson, Peter J.; Bansal, Shweta

2017-01-01

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

Unraveling the disease consequences and mechanisms of modular structure in animal social networks.

PubMed

Sah, Pratha; Leu, Stephan T; Cross, Paul C; Hudson, Peter J; Bansal, Shweta

2017-04-18

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

Modelling the evolution of a bi-partite network Peer referral in interlocking directorates*

PubMed Central

Edling, Christofer

2010-01-01

A central part of relational ties between social actors are constituted by shared affiliations and events. The action of joint participation reinforces personal ties between social actors as well as mutually shared values and norms that in turn perpetuate the patterns of social action that define groups. Therefore the study of bipartite networks is central to social science. Furthermore, the dynamics of these processes suggests that bipartite networks should not be considered static structures but rather be studied over time. In order to model the evolution of bipartite networks empirically we introduce a class of models and a Bayesian inference scheme that extends previous stochastic actor-oriented models for unimodal graphs. Contemporary research on interlocking directorates provides an area of research in which it seems reasonable to apply the model. Specifically, we address the question of how tie formation, i.e. director recruitment, contributes to the structural properties of the interlocking directorate network. For boards of directors on the Stockholm stock exchange we propose that a prolific mechanism in tie formation is that of peer referral. The results indicate that such a mechanism is present, generating multiple interlocks between boards. PMID:24944435

Exploring Self-Disclosure in Online Social Networks

ERIC Educational Resources Information Center

Velasco-Martin, Javier

2013-01-01

This project explores how experienced adult users of social media disclose personal information over online social networks (OSN). This work introduces a four-dimensional model to serve as a foundational framework for the study of online self-disclosure (OSD); these four dimensions are personal, social, technological and contextual, and support…

A common neural network differentially mediates direct and social fear learning.

PubMed

Lindström, Björn; Haaker, Jan; Olsson, Andreas

2018-02-15

Across species, fears often spread between individuals through social learning. Yet, little is known about the neural and computational mechanisms underlying social learning. Addressing this question, we compared social and direct (Pavlovian) fear learning showing that they showed indistinguishable behavioral effects, and involved the same cross-modal (self/other) aversive learning network, centered on the amygdala, the anterior insula (AI), and the anterior cingulate cortex (ACC). Crucially, the information flow within this network differed between social and direct fear learning. Dynamic causal modeling combined with reinforcement learning modeling revealed that the amygdala and AI provided input to this network during direct and social learning, respectively. Furthermore, the AI gated learning signals based on surprise (associability), which were conveyed to the ACC, in both learning modalities. Our findings provide insights into the mechanisms underlying social fear learning, with implications for understanding common psychological dysfunctions, such as phobias and other anxiety disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

Opinion formation and distribution in a bounded-confidence model on various networks

NASA Astrophysics Data System (ADS)

Meng, X. Flora; Van Gorder, Robert A.; Porter, Mason A.

2018-02-01

In the social, behavioral, and economic sciences, it is important to predict which individual opinions eventually dominate in a large population, whether there will be a consensus, and how long it takes for a consensus to form. Such ideas have been studied heavily both in physics and in other disciplines, and the answers depend strongly both on how one models opinions and on the network structure on which opinions evolve. One model that was created to study consensus formation quantitatively is the Deffuant model, in which the opinion distribution of a population evolves via sequential random pairwise encounters. To consider heterogeneity of interactions in a population along with social influence, we study the Deffuant model on various network structures (deterministic synthetic networks, random synthetic networks, and social networks constructed from Facebook data). We numerically simulate the Deffuant model and conduct regression analyses to investigate the dependence of the time to reach steady states on various model parameters, including a confidence bound for opinion updates, the number of participating entities, and their willingness to compromise. We find that network structure and parameter values both have important effects on the convergence time and the number of steady-state opinion groups. For some network architectures, we observe that the relationship between the convergence time and model parameters undergoes a transition at a critical value of the confidence bound. For some networks, the steady-state opinion distribution also changes from consensus to multiple opinion groups at this critical value.

Customer social network affects marketing strategy: A simulation analysis based on competitive diffusion model

NASA Astrophysics Data System (ADS)

Hou, Rui; Wu, Jiawen; Du, Helen S.

2017-03-01

To explain the competition phenomenon and results between QQ and MSN (China) in the Chinese instant messaging software market, this paper developed a new population competition model based on customer social network. The simulation results show that the firm whose product with greater network externality effect will gain more market share than its rival when the same marketing strategy is used. The firm with the advantage of time, derived from the initial scale effect will become more competitive than its rival when facing a group of common penguin customers within a social network, verifying the winner-take-all phenomenon in this case.

A Privacy Preservation Model for Health-Related Social Networking Sites.

PubMed

Li, Jingquan

2015-07-08

The increasing use of social networking sites (SNS) in health care has resulted in a growing number of individuals posting personal health information online. These sites may disclose users' health information to many different individuals and organizations and mine it for a variety of commercial and research purposes, yet the revelation of personal health information to unauthorized individuals or entities brings a concomitant concern of greater risk for loss of privacy among users. Many users join multiple social networks for different purposes and enter personal and other specific information covering social, professional, and health domains into other websites. Integration of multiple online and real social networks makes the users vulnerable to unintentional and intentional security threats and misuse. This paper analyzes the privacy and security characteristics of leading health-related SNS. It presents a threat model and identifies the most important threats to users and SNS providers. Building on threat analysis and modeling, this paper presents a privacy preservation model that incorporates individual self-protection and privacy-by-design approaches and uses the model to develop principles and countermeasures to protect user privacy. This study paves the way for analysis and design of privacy-preserving mechanisms on health-related SNS.

A Privacy Preservation Model for Health-Related Social Networking Sites

PubMed Central

2015-01-01

The increasing use of social networking sites (SNS) in health care has resulted in a growing number of individuals posting personal health information online. These sites may disclose users' health information to many different individuals and organizations and mine it for a variety of commercial and research purposes, yet the revelation of personal health information to unauthorized individuals or entities brings a concomitant concern of greater risk for loss of privacy among users. Many users join multiple social networks for different purposes and enter personal and other specific information covering social, professional, and health domains into other websites. Integration of multiple online and real social networks makes the users vulnerable to unintentional and intentional security threats and misuse. This paper analyzes the privacy and security characteristics of leading health-related SNS. It presents a threat model and identifies the most important threats to users and SNS providers. Building on threat analysis and modeling, this paper presents a privacy preservation model that incorporates individual self-protection and privacy-by-design approaches and uses the model to develop principles and countermeasures to protect user privacy. This study paves the way for analysis and design of privacy-preserving mechanisms on health-related SNS. PMID:26155953

Protective effects of social networks on disability among older adults in Spain.

PubMed

Escobar-Bravo, Miguel-Ángel; Puga-González, Dolores; Martín-Baranera, Monserrat

2012-01-01

The loss of autonomy at advanced ages is not only associated with ageing, but also with the characteristics of the physical and social environment. Recent investigations have shown that social networks, social engagement and participation act like predictors of disability among the elderly. The aim of this study is to determine whether social networks are related to the development and progression of disability in the early years of old age. The source of data is the first wave of the survey "Processes of Vulnerability among Spanish Elderly", carried out in 2005 to a sample of 1244 individuals. The population object of study is the cohort aged 70-74 years in metropolitan areas (Madrid and Barcelona) and not institutionalized. Disability is measured by the development of basic activities of daily life (ADL), and instrumental activities of daily life (IADL). The structural aspects of the social relationships are measured through the diversity of social networks and participation. We used the social network index (SNI). For each point over the SNI, the risk of developing any type of disability decreased by 49% (HR=0.51, 95%CI=0.31-0.82). The SNI was a decisive factor in all forecasting models constructed with some hazard ratios (HR) that ranged from 0.29 (95%CI=0.14-0.59) in the first model to 0.43 (95%CI 0.20-0.90) in the full model. The results of the present study showed a strong association between an active social life, emotional support provided by friends and confidents and disability. These findings suggest a protective effect of social networks on disability. Also, these results indicate that some family and emotional ties have a significant effect on both the prevalence and the incidence of disability. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services

PubMed Central

Fujiyama, Toshifumi; Matsui, Chihiro; Takemura, Akimichi

2016-01-01

We propose a power-law growth and decay model for posting data to social networking services before and after social events. We model the time series structure of deviations from the power-law growth and decay with a conditional Poisson autoregressive (AR) model. Online postings related to social events are described by five parameters in the power-law growth and decay model, each of which characterizes different aspects of interest in the event. We assess the validity of parameter estimates in terms of confidence intervals, and compare various submodels based on likelihoods and information criteria. PMID:27505155

Assortative model for social networks

NASA Astrophysics Data System (ADS)

Catanzaro, Michele; Caldarelli, Guido; Pietronero, Luciano

2004-09-01

In this Brief Report we present a version of a network growth model, generalized in order to describe the behavior of social networks. The case of study considered is the preprint archive at cul.arxiv.org. Each node corresponds to a scientist, and a link is present whenever two authors wrote a paper together. This graph is a nice example of degree-assortative network, that is, to say a network where sites with similar degree are connected to each other. The model presented is one of the few able to reproduce such behavior, giving some insight on the microscopic dynamics at the basis of the graph structure.

Emotion shapes the diffusion of moralized content in social networks

PubMed Central

Wills, Julian A.; Jost, John T.; Tucker, Joshua A.; Van Bavel, Jay J.

2017-01-01

Political debate concerning moralized issues is increasingly common in online social networks. However, moral psychology has yet to incorporate the study of social networks to investigate processes by which some moral ideas spread more rapidly or broadly than others. Here, we show that the expression of moral emotion is key for the spread of moral and political ideas in online social networks, a process we call “moral contagion.” Using a large sample of social media communications about three polarizing moral/political issues (n = 563,312), we observed that the presence of moral-emotional words in messages increased their diffusion by a factor of 20% for each additional word. Furthermore, we found that moral contagion was bounded by group membership; moral-emotional language increased diffusion more strongly within liberal and conservative networks, and less between them. Our results highlight the importance of emotion in the social transmission of moral ideas and also demonstrate the utility of social network methods for studying morality. These findings offer insights into how people are exposed to moral and political ideas through social networks, thus expanding models of social influence and group polarization as people become increasingly immersed in social media networks. PMID:28652356

Emotion shapes the diffusion of moralized content in social networks.

PubMed

Brady, William J; Wills, Julian A; Jost, John T; Tucker, Joshua A; Van Bavel, Jay J

2017-07-11

Political debate concerning moralized issues is increasingly common in online social networks. However, moral psychology has yet to incorporate the study of social networks to investigate processes by which some moral ideas spread more rapidly or broadly than others. Here, we show that the expression of moral emotion is key for the spread of moral and political ideas in online social networks, a process we call "moral contagion." Using a large sample of social media communications about three polarizing moral/political issues ( n = 563,312), we observed that the presence of moral-emotional words in messages increased their diffusion by a factor of 20% for each additional word. Furthermore, we found that moral contagion was bounded by group membership; moral-emotional language increased diffusion more strongly within liberal and conservative networks, and less between them. Our results highlight the importance of emotion in the social transmission of moral ideas and also demonstrate the utility of social network methods for studying morality. These findings offer insights into how people are exposed to moral and political ideas through social networks, thus expanding models of social influence and group polarization as people become increasingly immersed in social media networks.

Intergenerational Social Networks and Health Behaviors Among Children Living in Public Housing.

PubMed

Kennedy-Hendricks, Alene; Schwartz, Heather; Thornton, Rachel Johnson; Griffin, Beth Ann; Green, Harold D; Kennedy, David P; Burkhauser, Susan; Pollack, Craig Evan

2015-11-01

In a survey of families living in public housing, we investigated whether caretakers' social networks are linked with children's health status. In 2011, 209 children and their caretakers living in public housing in suburban Montgomery County, Maryland, were surveyed regarding their health and social networks. We used logistic regression models to examine the associations between the perceived health composition of caretaker social networks and corresponding child health characteristics (e.g., exercise, diet). With each 10% increase in the proportion of the caretaker's social network that exercised regularly, the child's odds of exercising increased by 34% (adjusted odds ratio = 1.34; 95% confidence interval = 1.07, 1.69) after the caretaker's own exercise behavior and the composition of the child's peer network had been taken into account. Although children's overweight or obese status was associated with caretakers' social networks, the results were no longer significant after adjustment for caretakers' own weight status. We found that caretaker social networks are independently associated with certain aspects of child health, suggesting the importance of the broader social environment for low-income children's health.

Targeted Victimization: Exploring Linear and Curvilinear Associations Between Social Network Prestige and Victimization.

PubMed

Andrews, Naomi C Z; Hanish, Laura D; Updegraff, Kimberly A; Martin, Carol Lynn; Santos, Carlos E

2016-09-01

Are early adolescent victims of peer-directed aggression youth who hold prominent positions in the social hierarchy or those who are socially marginalized? The present study tackles this question by testing for linear and curvilinear relationships between social network prestige and physical and relational forms of peer victimization for boys and girls. Participants were 952 middle schoolers (age range = 10-14 years; 49.9 % girls; 44 % Latino). Participants nominated victims and friends; friendship nominations were used to calculate social network prestige. Both hypotheses received support, with variation by gender. Girls high in social network prestige were highly victimized. For boys, those both high and low in social network prestige were highly victimized, whereas those at mid-levels of social network prestige were low in victimization. The findings are discussed in relation to a social dominance model of peer-directed aggression, and the practical implications are discussed in relation to protecting youth who are frequent targets of peer victimization.

Infant Joint Attention, Neural Networks and Social Cognition

PubMed Central

Mundy, Peter; Jarrold, William

2010-01-01

Neural network models of attention can provide a unifying approach to the study of human cognitive and emotional development (Posner & Rothbart, 2007). This paper we argue that a neural networks approach to the infant development of joint attention can inform our understanding of the nature of human social learning, symbolic thought process and social cognition. At its most basic, joint attention involves the capacity to coordinate one’s own visual attention with that of another person. We propose that joint attention development involves increments in the capacity to engage in simultaneous or parallel processing of information about one’s own attention and the attention of other people. Infant practice with joint attention is both a consequence and organizer of the development of a distributed and integrated brain network involving frontal and parietal cortical systems. This executive distributed network first serves to regulate the capacity of infants to respond to and direct the overt behavior of other people in order to share experience with others through the social coordination of visual attention. In this paper we describe this parallel and distributed neural network model of joint attention development and discuss two hypotheses that stem from this model. One is that activation of this distributed network during coordinated attention enhances to depth of information processing and encoding beginning in the first year of life. We also propose that with development joint attention becomes internalized as the capacity to socially coordinate mental attention to internal representations. As this occurs the executive joint attention network makes vital contributions to the development of human symbolic thinking and social cognition. PMID:20884172

Competition between Homophily and Information Entropy Maximization in Social Networks

PubMed Central

Zhao, Jichang; Liang, Xiao; Xu, Ke

2015-01-01

In social networks, it is conventionally thought that two individuals with more overlapped friends tend to establish a new friendship, which could be stated as homophily breeding new connections. While the recent hypothesis of maximum information entropy is presented as the possible origin of effective navigation in small-world networks. We find there exists a competition between information entropy maximization and homophily in local structure through both theoretical and experimental analysis. This competition suggests that a newly built relationship between two individuals with more common friends would lead to less information entropy gain for them. We demonstrate that in the evolution of the social network, both of the two assumptions coexist. The rule of maximum information entropy produces weak ties in the network, while the law of homophily makes the network highly clustered locally and the individuals would obtain strong and trust ties. A toy model is also presented to demonstrate the competition and evaluate the roles of different rules in the evolution of real networks. Our findings could shed light on the social network modeling from a new perspective. PMID:26334994

Perceived support from a caregiver's social ties predicts subsequent care-recipient health.

PubMed

Kelley, Dannielle E; Lewis, Megan A; Southwell, Brian G

2017-12-01

Most social support research has examined support from an individual patient perspective and does not model the broader social context of support felt by caregivers. Understanding how social support networks may complement healthcare services is critical, considering the aging population, as social support networks may be a valuable resource to offset some of the demands placed on the healthcare system. We sought to identify how caregivers' perceived organizational and interpersonal support from their social support network influences care-recipient health. We created a dyadic dataset of care-recipient and caregivers from the first two rounds of the National Health and Aging Trends survey (2011, 2012) and the first round of the associated National Study of Caregivers survey (2011). Using structural equation modeling, we explored how caregivers' perceived social support is associated with caregiver confidence to provide care, and is associated with care-recipient health outcomes at two time points. All data were analyzed in 2016. Social engagement with members from caregivers' social support networks was positively associated with caregiver confidence, and social engagement and confidence were positively associated with care-recipient health at time 1. Social engagement positively predicted patient health at time 2 controlling for time 1. Conversely, use of organizational support negatively predicted care-recipient health at time 2. Care-recipients experience better health outcomes when caregivers are able to be more engaged with members of their social support network.

In-House Communication Support System Based on the Information Propagation Model Utilizes Social Network

NASA Astrophysics Data System (ADS)

Takeuchi, Susumu; Teranishi, Yuuichi; Harumoto, Kaname; Shimojo, Shinji

Almost all companies are now utilizing computer networks to support speedier and more effective in-house information-sharing and communication. However, existing systems are designed to support communications only within the same department. Therefore, in our research, we propose an in-house communication support system which is based on the “Information Propagation Model (IPM).” The IPM is proposed to realize word-of-mouth communication in a social network, and to support information-sharing on the network. By applying the system in a real company, we found that information could be exchanged between different and unrelated departments, and such exchanges of information could help to build new relationships between the users who are apart on the social network.

Adolescents' Social Network Site Use, Peer Appearance-Related Feedback, and Body Dissatisfaction: Testing a Mediation Model.

PubMed

de Vries, Dian A; Peter, Jochen; de Graaf, Hanneke; Nikken, Peter

2016-01-01

Previous correlational research indicates that adolescent girls who use social network sites more frequently are more dissatisfied with their bodies. However, we know little about the causal direction of this relationship, the mechanisms underlying this relationship, and whether this relationship also occurs among boys to the same extent. The present two-wave panel study (18 month time lag) among 604 Dutch adolescents (aged 11-18; 50.7% female; 97.7% native Dutch) aimed to fill these gaps in knowledge. Structural equation modeling showed that social network site use predicted increased body dissatisfaction and increased peer influence on body image in the form of receiving peer appearance-related feedback. Peer appearance-related feedback did not predict body dissatisfaction and thus did not mediate the effect of social network site use on body dissatisfaction. Gender did not moderate the findings. Hence, social network sites can play an adverse role in the body image of both adolescent boys and girls.

A Web Service-Based Framework Model for People-Centric Sensing Applications Applied to Social Networking

PubMed Central

Nunes, David; Tran, Thanh-Dien; Raposo, Duarte; Pinto, André; Gomes, André; Silva, Jorge Sá

2012-01-01

As the Internet evolved, social networks (such as Facebook) have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users’ activities and locations, sharing this information amongst the user’s friends within a social networking site. We also present some screenshot results of our experimental prototype. PMID:22438732

A Web Service-based framework model for people-centric sensing applications applied to social networking.

PubMed

Nunes, David; Tran, Thanh-Dien; Raposo, Duarte; Pinto, André; Gomes, André; Silva, Jorge Sá

2012-01-01

As the Internet evolved, social networks (such as Facebook) have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users' activities and locations, sharing this information amongst the user's friends within a social networking site. We also present some screenshot results of our experimental prototype.

Infectious disease transmission and contact networks in wildlife and livestock.

PubMed

Craft, Meggan E

2015-05-26

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Infectious disease transmission and contact networks in wildlife and livestock

PubMed Central

Craft, Meggan E.

2015-01-01

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools. PMID:25870393

Exploring the Social Impact of Being a Typical Peer Model for Included Children with Autism Spectrum Disorder

PubMed Central

Locke, Jill; Fuller, Erin Rotheram; Kasari, Connie

2014-01-01

This study examined the social impact of being a typical peer model as part of a social skills intervention for children with autism spectrum disorder (ASD). Participants were drawn from a randomized-controlled-treatment trial that examined the effects of targeted interventions on the social networks of 60 elementary-aged children with ASD. Results demonstrated that typical peer models had higher social network centrality, received friendships, friendship quality, and less loneliness than non-peer models. Peer models were also more likely to be connected with children with ASD than non-peer models at baseline and exit. These results suggest that typical peers can be socially connected to children with ASD, as well as other classmates, and maintain a strong and positive role within the classroom. PMID:22215436

The Social Context Network Model in Psychiatric and Neurological Diseases.

PubMed

Baez, Sandra; García, Adolfo M; Ibanez, Agustín

2017-01-01

The role of contextual modulations has been extensively studied in basic sensory and cognitive processes. However, little is known about their impact on social cognition, let alone their disruption in disorders compromising such a domain. In this chapter, we flesh out the social context network model (SCNM), a neuroscientific proposal devised to address the issue. In SCNM terms, social context effects rely on a fronto-temporo-insular network in charge of (a) updating context cues to make predictions, (b) consolidating context-target associative learning, and (c) coordinating internal and external milieus. First, we characterize various social cognition domains as context-dependent phenomena. Then, we review behavioral and neural evidence of social context impairments in behavioral variant frontotemporal dementia (bvFTD) and autism spectrum disorder (ASD), highlighting their relation with key SCNM hubs. Next, we show that other psychiatric and neurological conditions involve context-processing impairments following damage to the brain regions included in the model. Finally, we call for an ecological approach to social cognition assessment, moving beyond widespread abstract and decontextualized methods.

The association between social network factors and mental health at different life stages.

PubMed

Levula, Andrew; Wilson, Andrew; Harré, Michael

2016-07-01

Psychosocial factors are important determinants of an individual's health. This study examines the association between health scores and social network factors on mental health across different life stages. Data were drawn from the Household Income and Labour Dynamics in Australia survey for adolescents (n = 1739), adults (n = 10,309) and seniors (n = 2287). Hierarchical regression modelling was applied to examine effects within and across age groups. All the variables were derived from the self-completion questionnaire. The social network factors were statistically significant predictors of mental health outcomes for all three life stages. For adolescents, the three social network factors were statistically significant with social isolation having the largest impact (β = -.284, p < .001), followed by social connection (β = .084, p < .001) and social trust having a similar effect (β = .073, p < .001). For adults social isolation had the highest impact (β = -.203, p < .001), followed by social connection (β = .110, p < .001) and social trust (β = .087, p < .001).The results for seniors were social isolation (β = -.188, p < .001), social connection (β = .147, p < .001) and social trust (β = .032, p < .05). After adding the social network factors, the models improved significantly with social isolation playing the most significant role across all life stages, whereas the other social network factors played a differentiated role depending upon the life stage. These findings have practical implications in the design of mental health interventions across different life stages.

Modelling of information diffusion on social networks with applications to WeChat

NASA Astrophysics Data System (ADS)

Liu, Liang; Qu, Bo; Chen, Bin; Hanjalic, Alan; Wang, Huijuan

2018-04-01

Traces of user activities recorded in online social networks open new possibilities to systematically understand the information diffusion process on social networks. From the online social network WeChat, we collected a large number of information cascade trees, each of which tells the spreading trajectory of a message/information such as which user creates the information and which users view or forward the information shared by which neighbours. In this work, we propose two heterogeneous non-linear models, one for the topologies of the information cascade trees and the other for the stochastic process of information diffusion on a social network. Both models are validated by the WeChat data in reproducing and explaining key features of cascade trees. Specifically, we apply the Random Recursive Tree (RRT) to model the growth of cascade trees. The RRT model could capture key features, i.e. the average path length and degree variance of a cascade tree in relation to the number of nodes (size) of the tree. Its single identified parameter quantifies the relative depth or broadness of the cascade trees and indicates that information propagates via a star-like broadcasting or viral-like hop by hop spreading. The RRT model explains the appearance of hubs, thus a possibly smaller average path length as the cascade size increases, as observed in WeChat. We further propose the stochastic Susceptible View Forward Removed (SVFR) model to depict the dynamic user behaviour including creating, viewing, forwarding and ignoring a message on a given social network. Beside the average path length and degree variance of the cascade trees in relation to their sizes, the SVFR model could further explain the power-law cascade size distribution in WeChat and unravel that a user with a large number of friends may actually have a smaller probability to read a message (s)he receives due to limited attention.

Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.

PubMed

Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L

2017-02-01

Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.

Complex networks generated by the Penna bit-string model: Emergence of small-world and assortative mixing

NASA Astrophysics Data System (ADS)

Li, Chunguang; Maini, Philip K.

2005-10-01

The Penna bit-string model successfully encompasses many phenomena of population evolution, including inheritance, mutation, evolution, and aging. If we consider social interactions among individuals in the Penna model, the population will form a complex network. In this paper, we first modify the Verhulst factor to control only the birth rate, and introduce activity-based preferential reproduction of offspring in the Penna model. The social interactions among individuals are generated by both inheritance and activity-based preferential increase. Then we study the properties of the complex network generated by the modified Penna model. We find that the resulting complex network has a small-world effect and the assortative mixing property.

How can social network analysis contribute to social behavior research in applied ethology?

PubMed

Makagon, Maja M; McCowan, Brenda; Mench, Joy A

2012-05-01

Social network analysis is increasingly used by behavioral ecologists and primatologists to describe the patterns and quality of interactions among individuals. We provide an overview of this methodology, with examples illustrating how it can be used to study social behavior in applied contexts. Like most kinds of social interaction analyses, social network analysis provides information about direct relationships (e.g. dominant-subordinate relationships). However, it also generates a more global model of social organization that determines how individual patterns of social interaction relate to individual and group characteristics. A particular strength of this approach is that it provides standardized mathematical methods for calculating metrics of sociality across levels of social organization, from the population and group levels to the individual level. At the group level these metrics can be used to track changes in social network structures over time, evaluate the effect of the environment on social network structure, or compare social structures across groups, populations or species. At the individual level, the metrics allow quantification of the heterogeneity of social experience within groups and identification of individuals who may play especially important roles in maintaining social stability or information flow throughout the network.

Information diffusion in structured online social networks

NASA Astrophysics Data System (ADS)

Li, Pei; Zhang, Yini; Qiao, Fengcai; Wang, Hui

2015-05-01

Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

The Social Origins of Networks and Diffusion.

PubMed

Centola, Damon

2015-03-01

Recent research on social contagion has demonstrated significant effects of network topology on the dynamics of diffusion. However, network topologies are not given a priori. Rather, they are patterns of relations that emerge from individual and structural features of society, such as population composition, group heterogeneity, homophily, and social consolidation. Following Blau and Schwartz, the author develops a model of social network formation that explores how social and structural constraints on tie formation generate emergent social topologies and then explores the effectiveness of these social networks for the dynamics of social diffusion. Results show that, at one extreme, high levels of consolidation can create highly balkanized communities with poor integration of shared norms and practices. As suggested by Blau and Schwartz, reducing consolidation creates more crosscutting circles and significantly improves the dynamics of social diffusion across the population. However, the author finds that further reducing consolidation creates highly intersecting social networks that fail to support the widespread diffusion of norms and practices, indicating that successful social diffusion can depend on moderate to high levels of structural consolidation.

A mismatch between supply and demand of social support in dementia care: a qualitative study on the perspectives of spousal caregivers and their social network members.

PubMed

Dam, Alieske E H; Boots, Lizzy M M; van Boxtel, Martin P J; Verhey, Frans R J; de Vugt, Marjolein E

2017-06-13

Access to social support contributes to feelings of independence and better social health. This qualitative study aims to investigate multi-informant perspectives on informal social support in dementia care networks. Ten spousal caregivers of people with dementia (PwD) completed an ecogram, a social network card and a semi-structured interview. The ecogram aimed to trigger subjective experiences regarding social support. Subsequently, 17 network members were interviewed. The qualitative analyses identified codes, categories, and themes. Sixth themes emerged: (1) barriers to ask for support; (2) facilitators to ask for support; (3) barriers to offer support; (4) facilitators to offer support; (5) a mismatch between supply and demand of social support; and (6) openness in communication to repair the imbalance. Integrating social network perspectives resulted in a novel model identifying a mismatch between the supply and demand of social support, strengthened by a cognitive bias: caregivers reported to think for other social network members and vice versa. Openness in communication in formal and informal care systems might repair this mismatch.

Simple deterministic models and applications. Comment on "Coupled disease-behavior dynamics on complex networks: A review" by Z. Wang et al.

NASA Astrophysics Data System (ADS)

Yang, Hyun Mo

2015-12-01

Currently, discrete modellings are largely accepted due to the access to computers with huge storage capacity and high performance processors and easy implementation of algorithms, allowing to develop and simulate increasingly sophisticated models. Wang et al. [7] present a review of dynamics in complex networks, focusing on the interaction between disease dynamics and human behavioral and social dynamics. By doing an extensive review regarding to the human behavior responding to disease dynamics, the authors briefly describe the complex dynamics found in the literature: well-mixed populations networks, where spatial structure can be neglected, and other networks considering heterogeneity on spatially distributed populations. As controlling mechanisms are implemented, such as social distancing due 'social contagion', quarantine, non-pharmaceutical interventions and vaccination, adaptive behavior can occur in human population, which can be easily taken into account in the dynamics formulated by networked populations.

Quantifying the propagation of distress and mental disorders in social networks.

PubMed

Scatà, Marialisa; Di Stefano, Alessandro; La Corte, Aurelio; Liò, Pietro

2018-03-22

Heterogeneity of human beings leads to think and react differently to social phenomena. Awareness and homophily drive people to weigh interactions in social multiplex networks, influencing a potential contagion effect. To quantify the impact of heterogeneity on spreading dynamics, we propose a model of coevolution of social contagion and awareness, through the introduction of statistical estimators, in a weighted multiplex network. Multiplexity of networked individuals may trigger propagation enough to produce effects among vulnerable subjects experiencing distress, mental disorder, which represent some of the strongest predictors of suicidal behaviours. The exposure to suicide is emotionally harmful, since talking about it may give support or inadvertently promote it. To disclose the complex effect of the overlapping awareness on suicidal ideation spreading among disordered people, we also introduce a data-driven approach by integrating different types of data. Our modelling approach unveils the relationship between distress and mental disorders propagation and suicidal ideation spreading, shedding light on the role of awareness in a social network for suicide prevention. The proposed model is able to quantify the impact of overlapping awareness on suicidal ideation spreading and our findings demonstrate that it plays a dual role on contagion, either reinforcing or delaying the contagion outbreak.

How to estimate the signs' configuration in the directed signed social networks?

NASA Astrophysics Data System (ADS)

Guo, Long; Gao, Fujuan; Jiang, Jian

2017-02-01

Inspired by the ensemble theory in statistical mechanics, we introduce a reshuffling approach to empirical analyze signs' configuration in the directed signed social networks of Epinions and Slashdots. In our reshuffling approach, each negative link has the reshuffling probability prs to exchange its sign with another positive link chosen randomly. Many reshuffled networks with different signs' configuration are built under different prss. For each reshuffled network, the entropies of the self social status are calculated and the opinion formation of the majority-rule model is analyzed. We find that Souts reach their own minimum values and the order parameter |m* | reaches its maximum value in the networks of Epinions and Slashdots without the reshuffling operation. Namely, individuals share the homogeneous properties of self social status and dynamic status in the real directed signed social networks. Our present work provides some interesting tools and perspective to understand the signs' configuration in signed social networks, especially in the online affiliation networks.

Aging and social networks in Spain: the importance of pubs and churches.

PubMed

Buz, José; Sanchez, Marta; Levenson, Michael R; Aldwin, Carolyn M

2014-01-01

We examined whether the social convoy model and socioemotional selectivity theory apply in collectivistic cultures by examining the contextual factors which are hypothesized to mediate age-related differences in social support in a collectivist European country. Five hundred Spanish community-dwelling older adults (Mean age = 74.78, SD = 7.76, range = 60-93) were interviewed to examine structural aspects of their social networks. We found that age showed highly complex relationships with network size and frequency of interaction, depending on the network circle and the mediation of cultural factors. Family structure was important for social relations in the inner circle, while pubs and churches were important for peripheral relations. Surprisingly, pub attendance was the most important variable for maintenance of social support of peripheral network members. In general, the results support the applicability of the social convoy and socioemotional selectivity constructs to social support among Spanish older adults.

Statistical Mechanics of Temporal and Interacting Networks

NASA Astrophysics Data System (ADS)

Zhao, Kun

In the last ten years important breakthroughs in the understanding of the topology of complexity have been made in the framework of network science. Indeed it has been found that many networks belong to the universality classes called small-world networks or scale-free networks. Moreover it was found that the complex architecture of real world networks strongly affects the critical phenomena defined on these structures. Nevertheless the main focus of the research has been the characterization of single and static networks. Recently, temporal networks and interacting networks have attracted large interest. Indeed many networks are interacting or formed by a multilayer structure. Example of these networks are found in social networks where an individual might be at the same time part of different social networks, in economic and financial networks, in physiology or in infrastructure systems. Moreover, many networks are temporal, i.e. the links appear and disappear on the fast time scale. Examples of these networks are social networks of contacts such as face-to-face interactions or mobile-phone communication, the time-dependent correlations in the brain activity and etc. Understanding the evolution of temporal and multilayer networks and characterizing critical phenomena in these systems is crucial if we want to describe, predict and control the dynamics of complex system. In this thesis, we investigate several statistical mechanics models of temporal and interacting networks, to shed light on the dynamics of this new generation of complex networks. First, we investigate a model of temporal social networks aimed at characterizing human social interactions such as face-to-face interactions and phone-call communication. Indeed thanks to the availability of data on these interactions, we are now in the position to compare the proposed model to the real data finding good agreement. Second, we investigate the entropy of temporal networks and growing networks , to provide a new framework to quantify the information encoded in these networks and to answer a fundamental problem in network science: how complex are temporal and growing networks. Finally, we consider two examples of critical phenomena in interacting networks. In particular, on one side we investigate the percolation of interacting networks by introducing antagonistic interactions. On the other side, we investigate a model of political election based on the percolation of antagonistic networks. The aim of this research is to show how antagonistic interactions change the physics of critical phenomena on interacting networks. We believe that the work presented in these thesis offers the possibility to appreciate the large variability of problems that can be addressed in the new framework of temporal and interacting networks.

Static, Dynamic and Semantic Dimensions: Towards a Multidisciplinary Approach of Social Networks Analysis

NASA Astrophysics Data System (ADS)

Thovex, Christophe; Trichet, Francky

The objective of our work is to extend static and dynamic models of Social Networks Analysis (SNA), by taking conceptual aspects of enterprises and institutions social graph into account. The originality of our multidisciplinary work is to introduce abstract notions of electro-physic to define new measures in SNA, for new decision-making functions dedicated to Human Resource Management (HRM). This paper introduces a multidimensional system and new measures: (1) a tension measure for social network analysis, (2) an electrodynamic, predictive and semantic system for recommendations on social graphs evolutions and (3) a reactance measure used to evaluate the individual stress at work of the members of a social network.

Incorporating Covariates into Stochastic Blockmodels

ERIC Educational Resources Information Center

Sweet, Tracy M.

2015-01-01

Social networks in education commonly involve some form of grouping, such as friendship cliques or teacher departments, and blockmodels are a type of statistical social network model that accommodate these grouping or blocks by assuming different within-group tie probabilities than between-group tie probabilities. We describe a class of models,…

The importance of including dynamic social networks when modeling epidemics of airborne infections: does increasing complexity increase accuracy?

PubMed

Blower, Sally; Go, Myong-Hyun

2011-07-19

Mathematical models are useful tools for understanding and predicting epidemics. A recent innovative modeling study by Stehle and colleagues addressed the issue of how complex models need to be to ensure accuracy. The authors collected data on face-to-face contacts during a two-day conference. They then constructed a series of dynamic social contact networks, each of which was used to model an epidemic generated by a fast-spreading airborne pathogen. Intriguingly, Stehle and colleagues found that increasing model complexity did not always increase accuracy. Specifically, the most detailed contact network and a simplified version of this network generated very similar results. These results are extremely interesting and require further exploration to determine their generalizability.

Information spreading on mobile communication networks: A new model that incorporates human behaviors

NASA Astrophysics Data System (ADS)

Ren, Fei; Li, Sai-Ping; Liu, Chuang

2017-03-01

Recently, there is a growing interest in the modeling and simulation based on real social networks among researchers in multi-disciplines. Using an empirical social network constructed from the calling records of a Chinese mobile service provider, we here propose a new model to simulate the information spreading process. This model takes into account two important ingredients that exist in real human behaviors: information prevalence and preferential spreading. The fraction of informed nodes when the system reaches an asymptotically stable state is primarily determined by information prevalence, and the heterogeneity of link weights would slow down the information diffusion. Moreover, the sizes of blind clusters which consist of connected uninformed nodes show a power-law distribution, and these uninformed nodes correspond to a particular portion of nodes which are located at special positions in the network, namely at the edges of large clusters or inside the clusters connected through weak links. Since the simulations are performed on a real world network, the results should be useful in the understanding of the influences of social network structures and human behaviors on information propagation.

Dynamic vs. static social networks in models of parasite transmission: predicting Cryptosporidium spread in wild lemurs.

PubMed

Springer, Andrea; Kappeler, Peter M; Nunn, Charles L

2017-05-01

Social networks provide an established tool to implement heterogeneous contact structures in epidemiological models. Dynamic temporal changes in contact structure and ranging behaviour of wildlife may impact disease dynamics. A consensus has yet to emerge, however, concerning the conditions in which network dynamics impact model outcomes, as compared to static approximations that average contact rates over longer time periods. Furthermore, as many pathogens can be transmitted both environmentally and via close contact, it is important to investigate the relative influence of both transmission routes in real-world populations. Here, we use empirically derived networks from a population of wild primates, Verreaux's sifakas (Propithecus verreauxi), and simulated networks to investigate pathogen spread in dynamic vs. static social networks. First, we constructed a susceptible-exposed-infected-recovered model of Cryptosporidium spread in wild Verreaux's sifakas. We incorporated social and environmental transmission routes and parameterized the model for two different climatic seasons. Second, we used simulated networks and greater variation in epidemiological parameters to investigate the conditions in which dynamic networks produce larger outbreak sizes than static networks. We found that average outbreak size of Cryptosporidium infections in sifakas was larger when the disease was introduced in the dry season than in the wet season, driven by an increase in home range overlap towards the end of the dry season. Regardless of season, dynamic networks always produced larger average outbreak sizes than static networks. Larger outbreaks in dynamic models based on simulated networks occurred especially when the probability of transmission and recovery were low. Variation in tie strength in the dynamic networks also had a major impact on outbreak size, while network modularity had a weaker influence than epidemiological parameters that determine transmission and recovery. Our study adds to emerging evidence that dynamic networks can change predictions of disease dynamics, especially if the disease shows low transmissibility and a long infectious period, and when environmental conditions lead to enhanced between-group contact after an infectious agent has been introduced. © 2016 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Theory of rumour spreading in complex social networks

NASA Astrophysics Data System (ADS)

Nekovee, M.; Moreno, Y.; Bianconi, G.; Marsili, M.

2007-01-01

We introduce a general stochastic model for the spread of rumours, and derive mean-field equations that describe the dynamics of the model on complex social networks (in particular, those mediated by the Internet). We use analytical and numerical solutions of these equations to examine the threshold behaviour and dynamics of the model on several models of such networks: random graphs, uncorrelated scale-free networks and scale-free networks with assortative degree correlations. We show that in both homogeneous networks and random graphs the model exhibits a critical threshold in the rumour spreading rate below which a rumour cannot propagate in the system. In the case of scale-free networks, on the other hand, this threshold becomes vanishingly small in the limit of infinite system size. We find that the initial rate at which a rumour spreads is much higher in scale-free networks than in random graphs, and that the rate at which the spreading proceeds on scale-free networks is further increased when assortative degree correlations are introduced. The impact of degree correlations on the final fraction of nodes that ever hears a rumour, however, depends on the interplay between network topology and the rumour spreading rate. Our results show that scale-free social networks are prone to the spreading of rumours, just as they are to the spreading of infections. They are relevant to the spreading dynamics of chain emails, viral advertising and large-scale information dissemination algorithms on the Internet.

Dense power-law networks and simplicial complexes

NASA Astrophysics Data System (ADS)

Courtney, Owen T.; Bianconi, Ginestra

2018-05-01

There is increasing evidence that dense networks occur in on-line social networks, recommendation networks and in the brain. In addition to being dense, these networks are often also scale-free, i.e., their degree distributions follow P (k ) ∝k-γ with γ ∈(1 ,2 ] . Models of growing networks have been successfully employed to produce scale-free networks using preferential attachment, however these models can only produce sparse networks as the numbers of links and nodes being added at each time step is constant. Here we present a modeling framework which produces networks that are both dense and scale-free. The mechanism by which the networks grow in this model is based on the Pitman-Yor process. Variations on the model are able to produce undirected scale-free networks with exponent γ =2 or directed networks with power-law out-degree distribution with tunable exponent γ ∈(1 ,2 ) . We also extend the model to that of directed two-dimensional simplicial complexes. Simplicial complexes are generalization of networks that can encode the many body interactions between the parts of a complex system and as such are becoming increasingly popular to characterize different data sets ranging from social interacting systems to the brain. Our model produces dense directed simplicial complexes with power-law distribution of the generalized out-degrees of the nodes.

Online social networking technologies, HIV knowledge, and sexual risk and testing behaviors among homeless youth.

PubMed

Young, Sean D; Rice, Eric

2011-02-01

This study evaluates associations between online social networking and sexual health behaviors among homeless youth in Los Angeles. We analyzed survey data from 201 homeless youth accessing services at a Los Angeles agency. Multivariate (regression and logistic) models assessed whether use of (and topics discussed on) online social networking technologies affect HIV knowledge, sexual risk behaviors, and testing for sexually transmitted infections (STIs). One set of results suggests that using online social networks for partner seeking (compared to not using the networks for seeking partners) is associated with increased sexual risk behaviors. Supporting data suggest that (1) using online social networks to talk about safe sex is associated with an increased likelihood of having met a recent sex partner online, and (2) having online sex partners and talking to friends on online social networks about drugs and partying is associated with increased exchange sex. However, results also suggest that online social network usage is associated with increased knowledge and HIV/STI prevention among homeless youth: (1) using online social networks to talk about love and safe sex is associated with increased knowledge about HIV, (2) using the networks to talk about love is associated with decreased exchange sex, and (3) merely being a member of an online social network is associated with increased likelihood of having previously tested for STIs. Taken together, this study suggests that online social networking and the topics discussed on these networks can potentially increase and decrease sexual risk behaviors depending on how the networks are used. Developing sexual health services and interventions on online social networks could reduce sexual risk behaviors.

Modeling Contagion Through Social Networks to Explain and Predict Gunshot Violence in Chicago, 2006 to 2014.

PubMed

Green, Ben; Horel, Thibaut; Papachristos, Andrew V

2017-03-01

Every day in the United States, more than 200 people are murdered or assaulted with a firearm. Little research has considered the role of interpersonal ties in the pathways through which gun violence spreads. To evaluate the extent to which the people who will become subjects of gun violence can be predicted by modeling gun violence as an epidemic that is transmitted between individuals through social interactions. This study was an epidemiological analysis of a social network of individuals who were arrested during an 8-year period in Chicago, Illinois, with connections between people who were arrested together for the same offense. Modeling of the spread of gunshot violence over the network was assessed using a probabilistic contagion model that assumed individuals were subject to risks associated with being arrested together, in addition to demographic factors, such as age, sex, and neighborhood residence. Participants represented a network of 138 163 individuals who were arrested between January 1, 2006, and March 31, 2014 (29.9% of all individuals arrested in Chicago during this period), 9773 of whom were subjects of gun violence. Individuals were on average 27 years old at the midpoint of the study, predominantly male (82.0%) and black (75.6%), and often members of a gang (26.2%). Explanation and prediction of becoming a subject of gun violence (fatal or nonfatal) using epidemic models based on person-to-person transmission through a social network. Social contagion accounted for 63.1% of the 11 123 gunshot violence episodes; subjects of gun violence were shot on average 125 days after their infector (the person most responsible for exposing the subject to gunshot violence). Some subjects of gun violence were shot more than once. Models based on both social contagion and demographics performed best; when determining the 1.0% of people (n = 1382) considered at highest risk to be shot each day, the combined model identified 728 subjects of gun violence (6.5%) compared with 475 subjects of gun violence (4.3%) for the demographics model (53.3% increase) and 589 subjects of gun violence (5.3%) for the social contagion model (23.6% increase). Gunshot violence follows an epidemic-like process of social contagion that is transmitted through networks of people by social interactions. Violence prevention efforts that account for social contagion, in addition to demographics, have the potential to prevent more shootings than efforts that focus on only demographics.

Phase Transition in Opinion Diffusion in Social Networks

DTIC Science & Technology

2012-05-01

the opinions of social agents diffuse in a network under a so-called hard-interaction model, in which the agents inter- act more strongly with...gent behavior. Index Terms— opinion diffusion , opinion dynamics, social net- works, phase transition, herding. 1. INTRODUCTION The study of the

Burstiness and tie activation strategies in time-varying social networks

NASA Astrophysics Data System (ADS)

Ubaldi, Enrico; Vezzani, Alessandro; Karsai, Márton; Perra, Nicola; Burioni, Raffaella

2017-04-01

The recent developments in the field of social networks shifted the focus from static to dynamical representations, calling for new methods for their analysis and modelling. Observations in real social systems identified two main mechanisms that play a primary role in networks’ evolution and influence ongoing spreading processes: the strategies individuals adopt when selecting between new or old social ties, and the bursty nature of the social activity setting the pace of these choices. We introduce a time-varying network model accounting both for ties selection and burstiness and we analytically study its phase diagram. The interplay of the two effects is non trivial and, interestingly, the effects of burstiness might be suppressed in regimes where individuals exhibit a strong preference towards previously activated ties. The results are tested against numerical simulations and compared with two empirical datasets with very good agreement. Consequently, the framework provides a principled method to classify the temporal features of real networks, and thus yields new insights to elucidate the effects of social dynamics on spreading processes.

Loneliness and social isolation among young and late middle-age adults: Associations with personal networks and social participation.

PubMed

Child, Stephanie T; Lawton, Leora

2017-11-24

Associations between social networks and loneliness or social isolation are well established among older adults. Yet, limited research examines personal networks and participation on perceived loneliness and social isolation as distinct experiences among younger adults. Accordingly, we explore relationships among objective and subjective measures of personal networks with loneliness and isolation, comparing a younger and older cohort. The UC Berkeley Social Networks Study offers unique cohort data on young (21-30 years old, n = 472) and late middle-age adults' (50-70 years old, n = 637) personal network characteristics, social participation, network satisfaction, relationship status, and days lonely and isolated via online survey or in-person interview. Negative binomial regression models were used to examine associations between social network characteristics, loneliness, and isolation by age group. Young adults reported twice as many days lonely and isolated than late middle-age adults, despite, paradoxically, having larger networks. For young adults, informal social participation and weekly religious attendance were associated with fewer days isolated. Among late middle-age adults, number of close kin and relationship status were associated with loneliness. Network satisfaction was associated with fewer days lonely or isolated among both age groups. Distinct network characteristics were associated with either loneliness or isolation for each cohort, suggesting network factors are independently associated with each outcome, and may fluctuate over time. Network satisfaction was associated with either loneliness or isolation among both cohorts, suggesting perceptions of social networks may be equally important as objective measures, and remain salient for loneliness and isolation throughout the life course.

Geographies of an Online Social Network.

PubMed

Lengyel, Balázs; Varga, Attila; Ságvári, Bence; Jakobi, Ákos; Kertész, János

2015-01-01

How is online social media activity structured in the geographical space? Recent studies have shown that in spite of earlier visions about the "death of distance", physical proximity is still a major factor in social tie formation and maintenance in virtual social networks. Yet, it is unclear, what are the characteristics of the distance dependence in online social networks. In order to explore this issue the complete network of the former major Hungarian online social network is analyzed. We find that the distance dependence is weaker for the online social network ties than what was found earlier for phone communication networks. For a further analysis we introduced a coarser granularity: We identified the settlements with the nodes of a network and assigned two kinds of weights to the links between them. When the weights are proportional to the number of contacts we observed weakly formed, but spatially based modules resemble to the borders of macro-regions, the highest level of regional administration in the country. If the weights are defined relative to an uncorrelated null model, the next level of administrative regions, counties are reflected.

Geographies of an Online Social Network

PubMed Central

Lengyel, Balázs; Varga, Attila; Ságvári, Bence; Jakobi, Ákos; Kertész, János

2015-01-01

How is online social media activity structured in the geographical space? Recent studies have shown that in spite of earlier visions about the “death of distance”, physical proximity is still a major factor in social tie formation and maintenance in virtual social networks. Yet, it is unclear, what are the characteristics of the distance dependence in online social networks. In order to explore this issue the complete network of the former major Hungarian online social network is analyzed. We find that the distance dependence is weaker for the online social network ties than what was found earlier for phone communication networks. For a further analysis we introduced a coarser granularity: We identified the settlements with the nodes of a network and assigned two kinds of weights to the links between them. When the weights are proportional to the number of contacts we observed weakly formed, but spatially based modules resemble to the borders of macro-regions, the highest level of regional administration in the country. If the weights are defined relative to an uncorrelated null model, the next level of administrative regions, counties are reflected. PMID:26359668

Modeling infection transmission in primate networks to predict centrality-based risk.

PubMed

Romano, Valéria; Duboscq, Julie; Sarabian, Cécile; Thomas, Elodie; Sueur, Cédric; MacIntosh, Andrew J J

2016-07-01

Social structure can theoretically regulate disease risk by mediating exposure to pathogens via social proximity and contact. Investigating the role of central individuals within a network may help predict infectious agent transmission as well as implement disease control strategies, but little is known about such dynamics in real primate networks. We combined social network analysis and a modeling approach to better understand transmission of a theoretical infectious agent in wild Japanese macaques, highly social animals which form extended but highly differentiated social networks. We collected focal data from adult females living on the islands of Koshima and Yakushima, Japan. Individual identities as well as grooming networks were included in a Markov graph-based simulation. In this model, the probability that an individual will transmit an infectious agent depends on the strength of its relationships with other group members. Similarly, its probability of being infected depends on its relationships with already infected group members. We correlated: (i) the percentage of subjects infected during a latency-constrained epidemic; (ii) the mean latency to complete transmission; (iii) the probability that an individual is infected first among all group members; and (iv) each individual's mean rank in the chain of transmission with different individual network centralities (eigenvector, strength, betweenness). Our results support the hypothesis that more central individuals transmit infections in a shorter amount of time and to more subjects but also become infected more quickly than less central individuals. However, we also observed that the spread of infectious agents on the Yakushima network did not always differ from expectations of spread on random networks. Generalizations about the importance of observed social networks in pathogen flow should thus be made with caution, since individual characteristics in some real world networks appear less relevant than they are in others in predicting disease spread. Am. J. Primatol. 78:767-779, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Geographical impacts on social networks from perspectives of space and place: an empirical study using mobile phone data

NASA Astrophysics Data System (ADS)

Shi, Li; Wu, Lun; Chi, Guanghua; Liu, Yu

2016-10-01

Space and place are two fundamental concepts in geography. Geographical factors have long been known as drivers of many aspects of people's social networks. But whether and how space and place affect social networks differently are still unclear. The widespread use of location-aware devices provides a novel source for distinguishing the mechanisms of their impacts on social networks. Using mobile phone data, this paper explores the effects of space and place on social networks. From the perspective of space, we confirm the distance decay effect in social networks, based on a comparison between synthetic social ties generated by a null model and actual social ties derived from real-world data. From the perspective of place, we introduce several measures to evaluate interactions between individuals and inspect the trio relationship including distance, spatio-temporal co-occurrence, and social ties. We found that people's interaction is a more important factor than spatial proximity, indicating that the spatial factor has a stronger impact on social networks in place compared to that in space. Furthermore, we verify the hypothesis that interactions play an important role in strengthening friendships.

Social traits, social networks and evolutionary biology.

PubMed

Fisher, D N; McAdam, A G

2017-12-01

The social environment is both an important agent of selection for most organisms, and an emergent property of their interactions. As an aggregation of interactions among members of a population, the social environment is a product of many sets of relationships and so can be represented as a network or matrix. Social network analysis in animals has focused on why these networks possess the structure they do, and whether individuals' network traits, representing some aspect of their social phenotype, relate to their fitness. Meanwhile, quantitative geneticists have demonstrated that traits expressed in a social context can depend on the phenotypes and genotypes of interacting partners, leading to influences of the social environment on the traits and fitness of individuals and the evolutionary trajectories of populations. Therefore, both fields are investigating similar topics, yet have arrived at these points relatively independently. We review how these approaches are diverged, and yet how they retain clear parallelism and so strong potential for complementarity. This demonstrates that, despite separate bodies of theory, advances in one might inform the other. Techniques in network analysis for quantifying social phenotypes, and for identifying community structure, should be useful for those studying the relationship between individual behaviour and group-level phenotypes. Entering social association matrices into quantitative genetic models may also reduce bias in heritability estimates, and allow the estimation of the influence of social connectedness on trait expression. Current methods for measuring natural selection in a social context explicitly account for the fact that a trait is not necessarily the property of a single individual, something the network approaches have not yet considered when relating network metrics to individual fitness. Harnessing evolutionary models that consider traits affected by genes in other individuals (i.e. indirect genetic effects) provides the potential to understand how entire networks of social interactions in populations influence phenotypes and predict how these traits may evolve. By theoretical integration of social network analysis and quantitative genetics, we hope to identify areas of compatibility and incompatibility and to direct research efforts towards the most promising areas. Continuing this synthesis could provide important insights into the evolution of traits expressed in a social context and the evolutionary consequences of complex and nuanced social phenotypes. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Cultural Geography Model Validation

DTIC Science & Technology

2010-03-01

the Cultural Geography Model (CGM), a government owned, open source multi - agent system utilizing Bayesian networks, queuing systems, the Theory of...referent determined either from theory or SME opinion. 4. CGM Overview The CGM is a government-owned, open source, data driven multi - agent social...HSCB, validation, social network analysis ABSTRACT: In the current warfighting environment , the military needs robust modeling and simulation (M&S

Clique Relaxations in Biological and Social Network Analysis Foundations and Algorithms

DTIC Science & Technology

2015-10-26

study of clique relaxation models arising in biological and social networks. This project examines the elementary clique-defining properties... elementary clique-defining properties inherently exploited in the available clique relaxation models and pro- poses a taxonomic framework that not...analyzes the elementary clique-defining properties implicitly exploited in the available clique relaxation models and proposes a taxonomic framework that

LATENT SPACE MODELS FOR MULTIVIEW NETWORK DATA

PubMed Central

Salter-Townshend, Michael; McCormick, Tyler H.

2018-01-01

Social relationships consist of interactions along multiple dimensions. In social networks, this means that individuals form multiple types of relationships with the same person (e.g., an individual will not trust all of his/her acquaintances). Statistical models for these data require understanding two related types of dependence structure: (i) structure within each relationship type, or network view, and (ii) the association between views. In this paper, we propose a statistical framework that parsimoniously represents dependence between relationship types while also maintaining enough flexibility to allow individuals to serve different roles in different relationship types. Our approach builds on work on latent space models for networks [see, e.g., J. Amer. Statist. Assoc. 97 (2002) 1090–1098]. These models represent the propensity for two individuals to form edges as conditionally independent given the distance between the individuals in an unobserved social space. Our work departs from previous work in this area by representing dependence structure between network views through a multivariate Bernoulli likelihood, providing a representation of between-view association. This approach infers correlations between views not explained by the latent space model. Using our method, we explore 6 multiview network structures across 75 villages in rural southern Karnataka, India [Banerjee et al. (2013)]. PMID:29721127

LATENT SPACE MODELS FOR MULTIVIEW NETWORK DATA.

PubMed

Salter-Townshend, Michael; McCormick, Tyler H

2017-09-01

Social relationships consist of interactions along multiple dimensions. In social networks, this means that individuals form multiple types of relationships with the same person (e.g., an individual will not trust all of his/her acquaintances). Statistical models for these data require understanding two related types of dependence structure: (i) structure within each relationship type, or network view, and (ii) the association between views. In this paper, we propose a statistical framework that parsimoniously represents dependence between relationship types while also maintaining enough flexibility to allow individuals to serve different roles in different relationship types. Our approach builds on work on latent space models for networks [see, e.g., J. Amer. Statist. Assoc. 97 (2002) 1090-1098]. These models represent the propensity for two individuals to form edges as conditionally independent given the distance between the individuals in an unobserved social space. Our work departs from previous work in this area by representing dependence structure between network views through a multivariate Bernoulli likelihood, providing a representation of between-view association. This approach infers correlations between views not explained by the latent space model. Using our method, we explore 6 multiview network structures across 75 villages in rural southern Karnataka, India [Banerjee et al. (2013)].

Coevolutionary dynamics of opinion propagation and social balance: The key role of small-worldness

NASA Astrophysics Data System (ADS)

Chen, Yan; Chen, Lixue; Sun, Xian; Zhang, Kai; Zhang, Jie; Li, Ping

2014-03-01

The propagation of various opinions in social networks, which influences human inter-relationships and even social structure, and hence is a most important part of social life. We have incorporated social balance into opinion propagation in social networks are influenced by social balance. The edges in networks can represent both friendly or hostile relations, and change with the opinions of individual nodes. We introduce a model to characterize the coevolutionary dynamics of these two dynamical processes on Watts-Strogatz (WS) small-world network. We employ two distinct evolution rules (i) opinion renewal; and (ii) relation adjustment. By changing the rewiring probability, and thus the small-worldness of the WS network, we found that the time for the system to reach balanced states depends critically on both the average path length and clustering coefficient of the network, which is different than other networked process like epidemic spreading. In particular, the system equilibrates most quickly when the underlying network demonstrates strong small-worldness, i.e., small average path lengths and large clustering coefficient. We also find that opinion clusters emerge in the process of the network approaching the global equilibrium, and a measure of global contrariety is proposed to quantify the balanced state of a social network.

The investigation of social networks based on multi-component random graphs

NASA Astrophysics Data System (ADS)

Zadorozhnyi, V. N.; Yudin, E. B.

2018-01-01

The methods of non-homogeneous random graphs calibration are developed for social networks simulation. The graphs are calibrated by the degree distributions of the vertices and the edges. The mathematical foundation of the methods is formed by the theory of random graphs with the nonlinear preferential attachment rule and the theory of Erdôs-Rényi random graphs. In fact, well-calibrated network graph models and computer experiments with these models would help developers (owners) of the networks to predict their development correctly and to choose effective strategies for controlling network projects.

Consumers don’t play dice, influence of social networks and advertisements

NASA Astrophysics Data System (ADS)

Groot, Robert D.

2006-05-01

Empirical data of supermarket sales show stylised facts that are similar to stock markets, with a broad (truncated) Lévy distribution of weekly sales differences in the baseline sales [R.D. Groot, Physica A 353 (2005) 501]. To investigate the cause of this, the influence of social interactions and advertisements are studied in an agent-based model of consumers in a social network. The influence of network topology was varied by using a small-world network, a random network and a Barabási-Albert network. The degree to which consumers value the opinion of their peers was also varied. On a small-world and random network we find a phase transition between an open market and a locked-in market that is similar to condensation in liquids. At the critical point, fluctuations become large and buying behaviour is strongly correlated. However, on the small world network the noise distribution at the critical point is Gaussian, and critical slowing down occurs which is not observed in supermarket sales. On a scale-free network, the model shows a transition between a gas-like phase and a glassy state, but at the transition point the noise amplitude is much larger than what is seen in supermarket sales. To explore the role of advertisements, a model is studied where imprints are placed on the minds of consumers that ripen when a decision for a product is made. The correct distribution of weekly sales returns follows naturally from this model, as well as the noise amplitude, the correlation time and cross-correlation of sales fluctuations. For particular parameter values, simulated sales correlation shows power-law decay in time. The model predicts that social interaction helps to prevent aversion, and that products are viewed more positively when their consumption rate is higher.

Opinion formation in a social network: The role of human activity

NASA Astrophysics Data System (ADS)

Grabowski, Andrzej

2009-03-01

The model of opinion formation in human population based on social impact theory is investigated numerically. On the basis of a database received from the on-line game server, we examine the structure of social network and human dynamics. We calculate the activity of individuals, i.e. the relative time devoted daily to interactions with others in the artificial society. We study the influence of correlation between the activity of an individual and its connectivity on the process of opinion formation. We find that such correlations have a significant influence on the temperature of the phase transition and the effect of the mass media, modeled as an external stimulation acting on the social network.

Nonequilibrium transitions in complex networks: A model of social interaction

NASA Astrophysics Data System (ADS)

Klemm, Konstantin; Eguíluz, Víctor M.; Toral, Raúl; San Miguel, Maxi

2003-02-01

We analyze the nonequilibrium order-disorder transition of Axelrod’s model of social interaction in several complex networks. In a small-world network, we find a transition between an ordered homogeneous state and a disordered state. The transition point is shifted by the degree of spatial disorder of the underlying network, the network disorder favoring ordered configurations. In random scale-free networks the transition is only observed for finite size systems, showing system size scaling, while in the thermodynamic limit only ordered configurations are always obtained. Thus, in the thermodynamic limit the transition disappears. However, in structured scale-free networks, the phase transition between an ordered and a disordered phase is restored.

Methods for inferring health-related social networks among coworkers from online communication patterns.

PubMed

Matthews, Luke J; DeWan, Peter; Rula, Elizabeth Y

2013-01-01

Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network.

Methods for Inferring Health-Related Social Networks among Coworkers from Online Communication Patterns

PubMed Central

Matthews, Luke J.; DeWan, Peter; Rula, Elizabeth Y.

2013-01-01

Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network. PMID:23418436

Depression and Chronic Health Conditions Among Latinos: The Role of Social Networks.

PubMed

Soto, Sandra; Arredondo, Elva M; Villodas, Miguel T; Elder, John P; Quintanar, Elena; Madanat, Hala

2016-12-01

The purpose of this study was to examine the "buffering hypothesis" of social network characteristics in the association between chronic conditions and depression among Latinos. Cross-sectional self-report data from the San Diego Prevention Research Center's community survey of Latinos were used (n = 393). Separate multiple logistic regression models tested the role of chronic conditions and social network characteristics in the likelihood of moderate-to-severe depressive symptoms. Having a greater proportion of the network comprised of friends increased the likelihood of depression among those with high cholesterol. Having a greater proportion of women in the social network was directly related to the increased likelihood of depression, regardless of the presence of chronic health conditions. Findings suggest that network characteristics may play a role in the link between chronic conditions and depression among Latinos. Future research should explore strategies targeting the social networks of Latinos to improve health outcomes.

Transition to parenthood: the role of social interaction and endogenous networks.

PubMed

Diaz, Belinda Aparicio; Fent, Thomas; Prskawetz, Alexia; Bernardi, Laura

2011-05-01

Empirical studies indicate that the transition to parenthood is influenced by an individual's peer group. To study the mechanisms creating interdependencies across individuals' transition to parenthood and its timing, we apply an agent-based simulation model. We build a one-sex model and provide agents with three different characteristics: age, intended education, and parity. Agents endogenously form their network based on social closeness. Network members may then influence the agents' transition to higher parity levels. Our numerical simulations indicate that accounting for social interactions can explain the shift of first-birth probabilities in Austria during the period 1984 to 2004. Moreover, we apply our model to forecast age-specific fertility rates up to 2016.

Inferring social status and rich club effects in enterprise communication networks.

PubMed

Dong, Yuxiao; Tang, Jie; Chawla, Nitesh V; Lou, Tiancheng; Yang, Yang; Wang, Bai

2015-01-01

Social status, defined as the relative rank or position that an individual holds in a social hierarchy, is known to be among the most important motivating forces in social behaviors. In this paper, we consider the notion of status from the perspective of a position or title held by a person in an enterprise. We study the intersection of social status and social networks in an enterprise. We study whether enterprise communication logs can help reveal how social interactions and individual status manifest themselves in social networks. To that end, we use two enterprise datasets with three communication channels--voice call, short message, and email--to demonstrate the social-behavioral differences among individuals with different status. We have several interesting findings and based on these findings we also develop a model to predict social status. On the individual level, high-status individuals are more likely to be spanned as structural holes by linking to people in parts of the enterprise networks that are otherwise not well connected to one another. On the community level, the principle of homophily, social balance and clique theory generally indicate a "rich club" maintained by high-status individuals, in the sense that this community is much more connected, balanced and dense. Our model can predict social status of individuals with 93% accuracy.

Multilevel examination of facility characteristics, social integration, and health for older adults living in nursing homes.

PubMed

Leedahl, Skye N; Chapin, Rosemary K; Little, Todd D

2015-01-01

Testing a model based on past research and theory, this study assessed relationships between facility characteristics (i.e., culture change efforts, social workers) and residents' social networks and social support across nursing homes; and examined relationships between multiple aspects of social integration (i.e., social networks, social capital, social engagement, social support) and mental and functional health for older adults in nursing homes. Data were collected at nursing homes using a planned missing data design with random sampling techniques. Data collection occurred at the individual-level through in-person structured interviews with older adult nursing home residents (N = 140) and at the facility-level (N = 30) with nursing home staff. The best fitting multilevel structural equation model indicated that the culture change subscale for relationships significantly predicted differences in residents' social networks. Additionally, social networks had a positive indirect relationship with mental and functional health among residents primarily via social engagement. Social capital had a positive direct relationship with both health outcomes. To predict better social integration and mental and functional health outcomes for nursing homes residents, study findings support prioritizing that close relationships exist among staff, residents, and the community as well as increased resident social engagement and social trust. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Modeling rises and falls in money addicted social hierarchies

NASA Astrophysics Data System (ADS)

Dybiec, Bartłomiej; Mitarai, Namiko; Sneppen, Kim

2014-08-01

The emergence of large communities is inherently associated with the creation of social structures. Connections between individuals are indispensable for cooperative action of agents building social groups. Moreover, social groups usually evolve and their structure changes over time. Consequently, an underlying network connecting individuals is not static, reflecting an ongoing adaptation to new conditions. The evolution of social connections is influenced by the relative position (hierarchy) of individuals building the system as well as by the availability of resources. We explore this aspect of human ambition by modeling the interplay of social networking and an uneven distribution of external resources. The model naturally generates social hierarchies. Remarkably, this social structure exhibits a rise-and-fall behavior. A well pronounced quasi-periodic dynamics, which is closely associated with the dissipation of resources that are needed to sustain the social links, is revealed.

Roles and functions of social networks among men who use drugs in ART initiation in Vietnam

PubMed Central

Latkin, Carl A.; Smith, M. Kumi; Ha, Tran Viet; Mo, Tran Thi; Zelaya, Carla; Sripaipan, Teerada; Le Minh, Nguyen; Quan, Vu Minh; Go, Vivian F.

2016-01-01

Support from social network members may help to facilitate access to HIV medical care, especially in low resourced communities. As part of a randomized clinical trial of a community-level stigma and risk reduction intervention in Thai Nguyen, Vietnam for people living with HIV who inject drugs (PWID), 341 participants were administered a baseline social network inventory. Network predictors of antiretroviral therapy (ART) initiation at the six-month follow-up were assessed. The social networks of PWID were sparse. Few participants who reported injectors in their networks also reported family members, whereas those who did not have injectors were more likely to report family members and network members providing emotional support and medical advice. In multivariate models, having at least one network member who provided medical advice predicted ART initiation at six months (OR=2.74, CI=1.20–6.28). These results suggest the importance of functional social support and network support mobilization for ART initiation among PWID. PMID:27125243

Roles and Functions of Social Networks Among Men Who Use Drugs in ART Initiation in Vietnam.

PubMed

Latkin, Carl A; Smith, M Kumi; Ha, Tran Viet; Mo, Tran Thi; Zelaya, Carla; Sripaipan, Teerada; Le Minh, Nguyen; Quan, Vu Minh; Go, Vivian F

2016-11-01

Support from social network members may help to facilitate access to HIV medical care, especially in low resourced communities. As part of a randomized clinical trial of a community-level stigma and risk reduction intervention in Thai Nguyen, Vietnam for people living with HIV who inject drugs (PWID), 341 participants were administered a baseline social network inventory. Network predictors of antiretroviral therapy (ART) initiation at the 6-month follow-up were assessed. The social networks of PWID were sparse. Few participants who reported injectors in their networks also reported family members, whereas those who did not have injectors were more likely to report family members and network members providing emotional support and medical advice. In multivariate models, having at least one network member who provided medical advice predicted ART initiation at 6 months (OR 2.74, CI 1.20-6.28). These results suggest the importance of functional social support and network support mobilization for ART initiation among PWID.

Measuring, Understanding, and Responding to Covert Social Networks: Passive and Active Tomography

DTIC Science & Technology

2017-11-11

practical algorithms for sociologically principled detection of small sub- networks. To detect “foreground” networks, we need two competing models...understanding of how to model “background” network clutter, leading to principled approaches to “foreground” sub-network detection. Before the MURI...no frameworks existed for network detection theory or goodness-of-fit, nor were models and algorithms coupled to sound sociological principles

Infant joint attention, neural networks and social cognition.

PubMed

Mundy, Peter; Jarrold, William

2010-01-01

Neural network models of attention can provide a unifying approach to the study of human cognitive and emotional development (Posner & Rothbart, 2007). In this paper we argue that a neural network approach to the infant development of joint attention can inform our understanding of the nature of human social learning, symbolic thought process and social cognition. At its most basic, joint attention involves the capacity to coordinate one's own visual attention with that of another person. We propose that joint attention development involves increments in the capacity to engage in simultaneous or parallel processing of information about one's own attention and the attention of other people. Infant practice with joint attention is both a consequence and an organizer of the development of a distributed and integrated brain network involving frontal and parietal cortical systems. This executive distributed network first serves to regulate the capacity of infants to respond to and direct the overt behavior of other people in order to share experience with others through the social coordination of visual attention. In this paper we describe this parallel and distributed neural network model of joint attention development and discuss two hypotheses that stem from this model. One is that activation of this distributed network during coordinated attention enhances the depth of information processing and encoding beginning in the first year of life. We also propose that with development, joint attention becomes internalized as the capacity to socially coordinate mental attention to internal representations. As this occurs the executive joint attention network makes vital contributions to the development of human symbolic thinking and social cognition. Copyright © 2010 Elsevier Ltd. All rights reserved.

The Social Context of Adolescent Friendships: Parents, Peers, and Romantic Partners

ERIC Educational Resources Information Center

Flynn, Heather Kohler; Felmlee, Diane H.; Conger, Rand D.

2017-01-01

We argue that adolescent friendships flourish, or wither, within the "linked lives" of other salient social network ties. Based on structural equation modeling with data from two time points, we find that young people tend to be in high-quality friendships when they are tightly embedded in their social network and receive social support…

Social support and ambulatory blood pressure: an examination of both receiving and giving.

PubMed

Piferi, Rachel L; Lawler, Kathleen A

2006-11-01

The relationship between the social network and physical health has been studied extensively and it has consistently been shown that individuals live longer, have fewer physical symptoms of illness, and have lower blood pressure when they are a member of a social network than when they are isolated. Much of the research has focused on the benefits of receiving social support from the network and the effects of giving to others within the network have been neglected. The goal of the present research was to systematically investigate the relationship between giving and ambulatory blood pressure. Systolic blood pressure, diastolic blood pressure, mean arterial pressure, and heart rate were recorded every 30 min during the day and every 60 min at night during a 24-h period. Linear mixed models analyses revealed that lower systolic and diastolic blood pressure and mean arterial pressure were related to giving social support. Furthermore, correlational analyses revealed that participants with a higher tendency to give social support reported greater received social support, greater self-efficacy, greater self-esteem, less depression, and less stress than participants with a lower tendency to give social support to others. Structural equation modeling was also used to test a proposed model that giving and receiving social support represent separate pathways predicting blood pressure and health. From this study, it appears that giving social support may represent a unique construct from receiving social support and may exert a unique effect on health.

Intergenerational Social Networks and Health Behaviors Among Children Living in Public Housing

PubMed Central

Schwartz, Heather; Thornton, Rachel Johnson; Griffin, Beth Ann; Green, Harold D.; Kennedy, David P.; Burkhauser, Susan; Pollack, Craig Evan

2015-01-01

Objectives. In a survey of families living in public housing, we investigated whether caretakers’ social networks are linked with children’s health status. Methods. In 2011, 209 children and their caretakers living in public housing in suburban Montgomery County, Maryland, were surveyed regarding their health and social networks. We used logistic regression models to examine the associations between the perceived health composition of caretaker social networks and corresponding child health characteristics (e.g., exercise, diet). Results. With each 10% increase in the proportion of the caretaker’s social network that exercised regularly, the child’s odds of exercising increased by 34% (adjusted odds ratio = 1.34; 95% confidence interval = 1.07, 1.69) after the caretaker’s own exercise behavior and the composition of the child’s peer network had been taken into account. Although children’s overweight or obese status was associated with caretakers’ social networks, the results were no longer significant after adjustment for caretakers’ own weight status. Conclusions. We found that caretaker social networks are independently associated with certain aspects of child health, suggesting the importance of the broader social environment for low-income children’s health. PMID:26378821

Modular and hierarchical structure of social contact networks

NASA Astrophysics Data System (ADS)

Ge, Yuanzheng; Song, Zhichao; Qiu, Xiaogang; Song, Hongbin; Wang, Yong

2013-10-01

Social contact networks exhibit overlapping qualities of communities, hierarchical structure and spatial-correlated nature. We propose a mixing pattern of modular and growing hierarchical structures to reconstruct social contact networks by using an individual’s geospatial distribution information in the real world. The hierarchical structure of social contact networks is defined based on the spatial distance between individuals, and edges among individuals are added in turn from the modular layer to the highest layer. It is a gradual process to construct the hierarchical structure: from the basic modular model up to the global network. The proposed model not only shows hierarchically increasing degree distribution and large clustering coefficients in communities, but also exhibits spatial clustering features of individual distributions. As an evaluation of the method, we reconstruct a hierarchical contact network based on the investigation data of a university. Transmission experiments of influenza H1N1 are carried out on the generated social contact networks, and results show that the constructed network is efficient to reproduce the dynamic process of an outbreak and evaluate interventions. The reproduced spread process exhibits that the spatial clustering of infection is accordant with the clustering of network topology. Moreover, the effect of individual topological character on the spread of influenza is analyzed, and the experiment results indicate that the spread is limited by individual daily contact patterns and local clustering topology rather than individual degree.

Time Allocation in Social Networks: Correlation Between Social Structure and Human Communication Dynamics

NASA Astrophysics Data System (ADS)

Miritello, Giovanna; Lara, Rubén; Moro, Esteban

Recent research has shown the deep impact of the dynamics of human interactions (or temporal social networks) on the spreading of information, opinion formation, etc. In general, the bursty nature of human interactions lowers the interaction between people to the extent that both the speed and reach of information diffusion are diminished. Using a large database of 20 million users of mobile phone calls we show evidence this effect is not homogeneous in the social network but in fact, there is a large correlation between this effect and the social topological structure around a given individual. In particular, we show that social relations of hubs in a network are relatively weaker from the dynamical point than those that are poorer connected in the information diffusion process. Our results show the importance of the temporal patterns of communication when analyzing and modeling dynamical process on social networks.

Modeling of information diffusion in Twitter-like social networks under information overload.

PubMed

Li, Pei; Li, Wei; Wang, Hui; Zhang, Xin

2014-01-01

Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. Users are classified into different types according to their in-degrees and out-degrees, and user behaviors are generalized into two categories: generating and forwarding. View scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated by a given type user is adopted to characterize the information diffusion efficiency, which is calculated theoretically. To verify the accuracy of theoretical analysis results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of importance to understand the diffusion dynamics in social networks, and this analysis framework can be extended to consider more realistic situations.

Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload

PubMed Central

Li, Wei

2014-01-01

Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. Users are classified into different types according to their in-degrees and out-degrees, and user behaviors are generalized into two categories: generating and forwarding. View scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated by a given type user is adopted to characterize the information diffusion efficiency, which is calculated theoretically. To verify the accuracy of theoretical analysis results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of importance to understand the diffusion dynamics in social networks, and this analysis framework can be extended to consider more realistic situations. PMID:24795541

Social networks and spreading of epidemics

NASA Astrophysics Data System (ADS)

Trimper, Steffen; Zheng, Dafang; Brandau, Marian

2004-05-01

Epidemiological processes are studied within a recently proposed social network model using the susceptible-infected-refractory dynamics (SIR) of an epidemic. Within the network model, a population of individuals may be characterized by H independent hierarchies or dimensions, each of which consists of groupings of individuals into layers of subgroups. Detailed numerical simulations reveals that for H > 1, the global spreading results regardless of the degree of homophily α of the individuals forming a social circle. For H = 1, a transition from a global to a local spread occurs as the population becomes decomposed into increasingly homophilous groups. Multiple dimensions in classifying individuals (nodes) thus make a society (computer network) highly susceptible to large scale outbreaks of infectious diseases (viruses). The SIR-model can be extended by the inclusion of waiting times resulting in modified distribution function of the recovered.

Loneliness and depression in the elderly: the role of social network.

PubMed

Domènech-Abella, Joan; Lara, Elvira; Rubio-Valera, Maria; Olaya, Beatriz; Moneta, Maria Victoria; Rico-Uribe, Laura Alejandra; Ayuso-Mateos, Jose Luis; Mundó, Jordi; Haro, Josep Maria

2017-04-01

Loneliness and depression are associated, in particular in older adults. Less is known about the role of social networks in this relationship. The present study analyzes the influence of social networks in the relationship between loneliness and depression in the older adult population in Spain. A population-representative sample of 3535 adults aged 50 years and over from Spain was analyzed. Loneliness was assessed by means of the three-item UCLA Loneliness Scale. Social network characteristics were measured using the Berkman-Syme Social Network Index. Major depression in the previous 12 months was assessed with the Composite International Diagnostic Interview (CIDI). Logistic regression models were used to analyze the survey data. Feelings of loneliness were more prevalent in women, those who were younger (50-65), single, separated, divorced or widowed, living in a rural setting, with a lower frequency of social interactions and smaller social network, and with major depression. Among people feeling lonely, those with depression were more frequently married and had a small social network. Among those not feeling lonely, depression was associated with being previously married. In depressed people, feelings of loneliness were associated with having a small social network; while among those without depression, feelings of loneliness were associated with being married. The type and size of social networks have a role in the relationship between loneliness and depression. Increasing social interaction may be more beneficial than strategies based on improving maladaptive social cognition in loneliness to reduce the prevalence of depression among Spanish older adults.

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS

PubMed Central

Almquist, Zack W.; Butts, Carter T.

2015-01-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach. PMID:26120218

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS.

PubMed

Almquist, Zack W; Butts, Carter T

2014-08-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.

Network Interventions on Physical Activity in an Afterschool Program: An Agent-Based Social Network Study

PubMed Central

Zhang, Jun; Shoham, David A.; Tesdahl, Eric

2015-01-01

Objectives. We studied simulated interventions that leveraged social networks to increase physical activity in children. Methods. We studied a real-world social network of 81 children (average age = 7.96 years) who lived in low socioeconomic status neighborhoods, and attended public schools and 1 of 2 structured afterschool programs. The sample was ethnically diverse, and 44% were overweight or obese. We used social network analysis and agent-based modeling simulations to test whether implementing a network intervention would increase children’s physical activity. We tested 3 intervention strategies. Results. The intervention that targeted opinion leaders was effective in increasing the average level of physical activity across the entire network. However, the intervention that targeted the most sedentary children was the best at increasing their physical activity levels. Conclusions. Which network intervention to implement depends on whether the goal is to shift the entire distribution of physical activity or to influence those most adversely affected by low physical activity. Agent-based modeling could be an important complement to traditional project planning tools, analogous to sample size and power analyses, to help researchers design more effective interventions for increasing children’s physical activity. PMID:25689202

Network marketing with bounded rationality and partial information

NASA Astrophysics Data System (ADS)

Kiet, Hoang Anh Tuan; Kim, Beom Jun

2008-08-01

Network marketing has been proposed and used as a way to spread the product information to consumers through social connections. We extend the previous game model of the network marketing on a small-world tree network and propose two games: In the first model with the bounded rationality, each consumer makes purchase decision stochastically, while in the second model, consumers get only partial information due to the finite length of social connections. Via extensive numerical simulations, we find that as the rationality is enhanced not only the consumer surplus but also the firm’s profit is increased. The implication of our results is also discussed.

Investigating the Associations between Ethnic Networks, Community Social Capital, and Physical Health among Marriage Migrants in Korea.

PubMed

Kim, Harris Hyun-Soo

2018-01-17

This study examines factors associated with the physical health of Korea's growing immigrant population. Specifically, it focuses on the associations between ethnic networks, community social capital, and self-rated health (SRH) among female marriage migrants. For empirical testing, secondary analysis of a large nationally representative sample (NSMF 2009) is conducted. Given the clustered data structure (individuals nested in communities), a series of two-level random intercepts and slopes models are fitted to probe the relationships between SRH and interpersonal (bonding and bridging) networks among foreign-born wives in Korea. In addition to direct effects, cross-level interaction effects are investigated using hierarchical linear modeling. While adjusting for confounders, bridging (inter-ethnic) networks are significantly linked with better health. Bonding (co-ethnic) networks, to the contrary, are negatively associated with immigrant health. Net of individual-level covariates, living in a commuijnity with more aggregate bridging social capital is positively linked with health. Community-level bonding social capital, however, is not a significant predictor. Lastly, two cross-level interaction terms are found. First, the positive relationship between bridging network and health is stronger in residential contexts with more aggregate bridging social capital. Second, it is weaker in communities with more aggregate bonding social capital.

The impact of a social network intervention on retention in Belgian therapeutic communities: a quasi-experimental study.

PubMed

Soyez, Veerle; De Leon, George; Broekaert, Eric; Rosseel, Yves

2006-07-01

Although numerous studies recognize the importance of social network support in engaging substance abusers into treatment, there is only limited knowledge of the impact of network involvement and support during treatment. The primary objective of this research was to enhance retention in Therapeutic Community treatment utilizing a social network intervention. The specific goals of this study were (1) to determine whether different pre-treatment factors predicted treatment retention in a Therapeutic Community; and (2) to determine whether participation of significant others in a social network intervention predicted treatment retention. Consecutive admissions to four long-term residential Therapeutic Communities were assessed at intake (n = 207); the study comprised a mainly male (84.9%) sample of polydrug (41.1%) and opiate (20.8%) abusers, of whom 64.4% had ever injected drugs. Assessment involved the European version of the Addiction Severity Index (EuropASI), the Circumstances, Motivation, Readiness scales (CMR), the Dutch version of the family environment scale (GKS/FES) and an in-depth interview on social network structure and perceived social support. Network members of different cohorts were assigned to a social network intervention, which consisted of three elements (a video, participation at an induction day and participation in a discussion session). Hierarchical regression analyses showed that client-perceived social support (F1,198 = 10.9, P = 0.001) and treatment motivation and readiness (F1,198 = 8.8; P = 0.003) explained a significant proportion of the variance in treatment retention (model fit: F7,197 = 4.4; P = 0.000). By including the variable 'significant others' participation in network intervention' (network involvement) in the model, the fit clearly improved (F1,197 = 6.2; P = 0.013). At the same time, the impact of perceived social support decreased (F1,197 = 2.9; P = 0.091). Participation in the social network intervention was associated with improved treatment retention controlling for other client characteristics. This suggests that the intervention may be of benefit in the treatment of addicted individuals.

Social networks: Evolving graphs with memory dependent edges

NASA Astrophysics Data System (ADS)

Grindrod, Peter; Parsons, Mark

2011-10-01

The plethora of digital communication technologies, and their mass take up, has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the existence or otherwise of certain infinite products and series involving age dependent model parameters. We show how to differentiate between the alternatives based on a finite set of observations. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

An opinion-driven behavioral dynamics model for addictive behaviors

NASA Astrophysics Data System (ADS)

Moore, Thomas W.; Finley, Patrick D.; Apelberg, Benjamin J.; Ambrose, Bridget K.; Brodsky, Nancy S.; Brown, Theresa J.; Husten, Corinne; Glass, Robert J.

2015-04-01

We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual's behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters provide targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. This has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.

Effects of Network Structure, Competition and Memory Time on Social Spreading Phenomena

NASA Astrophysics Data System (ADS)

Gleeson, James P.; O'Sullivan, Kevin P.; Baños, Raquel A.; Moreno, Yamir

2016-04-01

Online social media has greatly affected the way in which we communicate with each other. However, little is known about what fundamental mechanisms drive dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior that is analytically tractable and that can reproduce several characteristics of empirical micro-blogging data on hashtag usage, such as (time-dependent) heavy-tailed distributions of meme popularity. The presented framework constitutes a null model for social spreading phenomena that, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.

Computational Models for Belief Revision, Group Decision-Making and Cultural Shifts

DTIC Science & Technology

2010-10-25

34social" networks; the green numbers are pseudo-trees or artificial (non-social) constructions. The dashed blue line indicates the range of Erdos- Renyi ...non-social networks such as Erdos- Renyi random graphs or the more passive non-cognitive spreading of disease or information flow, As mentioned

Contagion on complex networks with persuasion

NASA Astrophysics Data System (ADS)

Huang, Wei-Min; Zhang, Li-Jie; Xu, Xin-Jian; Fu, Xinchu

2016-03-01

The threshold model has been widely adopted as a classic model for studying contagion processes on social networks. We consider asymmetric individual interactions in social networks and introduce a persuasion mechanism into the threshold model. Specifically, we study a combination of adoption and persuasion in cascading processes on complex networks. It is found that with the introduction of the persuasion mechanism, the system may become more vulnerable to global cascades, and the effects of persuasion tend to be more significant in heterogeneous networks than those in homogeneous networks: a comparison between heterogeneous and homogeneous networks shows that under weak persuasion, heterogeneous networks tend to be more robust against random shocks than homogeneous networks; whereas under strong persuasion, homogeneous networks are more stable. Finally, we study the effects of adoption and persuasion threshold heterogeneity on systemic stability. Though both heterogeneities give rise to global cascades, the adoption heterogeneity has an overwhelmingly stronger impact than the persuasion heterogeneity when the network connectivity is sufficiently dense.

Contagion on complex networks with persuasion

PubMed Central

Huang, Wei-Min; Zhang, Li-Jie; Xu, Xin-Jian; Fu, Xinchu

2016-01-01

The threshold model has been widely adopted as a classic model for studying contagion processes on social networks. We consider asymmetric individual interactions in social networks and introduce a persuasion mechanism into the threshold model. Specifically, we study a combination of adoption and persuasion in cascading processes on complex networks. It is found that with the introduction of the persuasion mechanism, the system may become more vulnerable to global cascades, and the effects of persuasion tend to be more significant in heterogeneous networks than those in homogeneous networks: a comparison between heterogeneous and homogeneous networks shows that under weak persuasion, heterogeneous networks tend to be more robust against random shocks than homogeneous networks; whereas under strong persuasion, homogeneous networks are more stable. Finally, we study the effects of adoption and persuasion threshold heterogeneity on systemic stability. Though both heterogeneities give rise to global cascades, the adoption heterogeneity has an overwhelmingly stronger impact than the persuasion heterogeneity when the network connectivity is sufficiently dense. PMID:27029498

Contagion on complex networks with persuasion.

PubMed

Huang, Wei-Min; Zhang, Li-Jie; Xu, Xin-Jian; Fu, Xinchu

2016-03-31

The threshold model has been widely adopted as a classic model for studying contagion processes on social networks. We consider asymmetric individual interactions in social networks and introduce a persuasion mechanism into the threshold model. Specifically, we study a combination of adoption and persuasion in cascading processes on complex networks. It is found that with the introduction of the persuasion mechanism, the system may become more vulnerable to global cascades, and the effects of persuasion tend to be more significant in heterogeneous networks than those in homogeneous networks: a comparison between heterogeneous and homogeneous networks shows that under weak persuasion, heterogeneous networks tend to be more robust against random shocks than homogeneous networks; whereas under strong persuasion, homogeneous networks are more stable. Finally, we study the effects of adoption and persuasion threshold heterogeneity on systemic stability. Though both heterogeneities give rise to global cascades, the adoption heterogeneity has an overwhelmingly stronger impact than the persuasion heterogeneity when the network connectivity is sufficiently dense.

Sustainable use of renewable resources in a stylized social-ecological network model under heterogeneous resource distribution

NASA Astrophysics Data System (ADS)

Barfuss, Wolfram; Donges, Jonathan F.; Wiedermann, Marc; Lucht, Wolfgang

2017-04-01

Human societies depend on the resources ecosystems provide. Particularly since the last century, human activities have transformed the relationship between nature and society at a global scale. We study this coevolutionary relationship by utilizing a stylized model of private resource use and social learning on an adaptive network. The latter process is based on two social key dynamics beyond economic paradigms: boundedly rational imitation of resource use strategies and homophily in the formation of social network ties. The private and logistically growing resources are harvested with either a sustainable (small) or non-sustainable (large) effort. We show that these social processes can have a profound influence on the environmental state, such as determining whether the private renewable resources collapse from overuse or not. Additionally, we demonstrate that heterogeneously distributed regional resource capacities shift the critical social parameters where this resource extraction system collapses. We make these points to argue that, in more advanced coevolutionary models of the planetary social-ecological system, such socio-cultural phenomena as well as regional resource heterogeneities should receive attention in addition to the processes represented in established Earth system and integrated assessment models.

Tracking Effects of Problematic Social Networking on Adolescent Psychopathology: The Mediating Role of Sleep Disruptions.

PubMed

Vernon, Lynette; Modecki, Kathryn L; Barber, Bonnie L

2017-01-01

Concerns are growing about adolescents' problematic social networking and possible links to depressed mood and externalizing behavior. Yet there remains little understanding of underlying processes that may account for these associations, including the mediating role of sleep disruption. This study tests this putative mediating process and examines change in problematic social networking investment and disrupted sleep, in relation to change in depressed mood and externalizing behavior. A sample of 874 students (41% male; 57.2% Caucasian; baseline M age = 14.4 years) from 27 high schools were surveyed. Participants' problematic social networking, sleep disruption, and psychopathology (depressed mood, externalizing behaviors) were measured annually over 3 years. Longitudinal mediation was tested using latent trajectories of problematic social networking use, sleep disruption, and psychopathology. Both problematic social networking and sleep disruption underwent positive linear growth over time. Adolescents who increasingly invested in social networking reported increased depressed mood, with around 53% of this association explained by the indirect effect of increased sleep disruptions. Further, adolescents who increasingly invested in social networking also reported increased externalizing behavior; some of this relation was explained (13%) via increased sleep disruptions. However an alternative model in which increased externalizing was associated with increased social networking, mediated by sleep disruptions, indicated a reciprocal relation of similar magnitude. It is important for parents, teachers, and psychologists to minimize the negative effects of social networking on adolescents' psychopathology. Interventions should potentially target promoting healthy sleep habits through reductions in social networking investment and rescheduling usage away from bedtime.

Social adaptation in multi-agent model of linguistic categorization is affected by network information flow.

PubMed

Zubek, Julian; Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz

2017-01-01

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems.

Social adaptation in multi-agent model of linguistic categorization is affected by network information flow

PubMed Central

Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz

2017-01-01

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems. PMID:28809957

SSIC model: A multi-layer model for intervention of online rumors spreading

NASA Astrophysics Data System (ADS)

Tian, Ru-Ya; Zhang, Xue-Fu; Liu, Yi-Jun

2015-06-01

SIR model is a classical model to simulate rumor spreading, while the supernetwork is an effective tool for modeling complex systems. Based on the Opinion SuperNetwork involving Social Sub-network, Environmental Sub-network, Psychological Sub-network, and Viewpoint Sub-network, drawing from the modeling idea of SIR model, this paper designs super SIC model (SSIC model) and its evolution rules, and also analyzes intervention effects on public opinion of four elements of supernetwork, which are opinion agent, opinion environment, agent's psychology and viewpoint. Studies show that, the SSIC model based on supernetwork has effective intervention effects on rumor spreading. It is worth noting that (i) identifying rumor spreaders in Social Sub-network and isolating them can achieve desired intervention results, (ii) improving environmental information transparency so that the public knows as much information as possible to reduce the rumors is a feasible way to intervene, (iii) persuading wavering neutrals has better intervention effects than clarifying rumors already spread everywhere, so rumors should be intervened in properly in time by psychology counseling.

Building a sense of virtual community: the role of the features of social networking sites.

PubMed

Chen, Chi-Wen; Lin, Chiun-Sin

2014-07-01

In recent years, social networking sites have received increased attention because of the potential of this medium to transform business by building virtual communities. However, theoretical and empirical studies investigating how specific features of social networking sites contribute to building a sense of virtual community (SOVC)-an important dimension of a successful virtual community-are rare. Furthermore, SOVC scales have been developed, and research on this issue has been called for, but few studies have heeded this call. On the basis of prior literature, this study proposes that perceptions of the three most salient features of social networking sites-system quality (SQ), information quality (IQ), and social information exchange (SIE)-play a key role in fostering SOVC. In particular, SQ is proposed to increase IQ and SIE, and SIE is proposed to enhance IQ, both of which thereafter build SOVC. The research model was examined in the context of Facebook, one of the most popular social networking sites in the world. We adopted Blanchard's scales to measure SOVC. Data gathered using a Web-based questionnaire, and analyzed with partial least squares, were utilized to test the model. The results demonstrate that SIE, SQ, and IQ are the factors that form SOVC. The findings also suggest that SQ plays a fundamental role in supporting SIE and IQ in social networking sites. Implications for theory, practice, and future research directions are discussed.

Mass media influence spreading in social networks with community structure

NASA Astrophysics Data System (ADS)

Candia, Julián; Mazzitello, Karina I.

2008-07-01

We study an extension of Axelrod's model for social influence, in which cultural drift is represented as random perturbations, while mass media are introduced by means of an external field. In this scenario, we investigate how the modular structure of social networks affects the propagation of mass media messages across a society. The community structure of social networks is represented by coupled random networks, in which two random graphs are connected by intercommunity links. Considering inhomogeneous mass media fields, we study the conditions for successful message spreading and find a novel phase diagram in the multidimensional parameter space. These findings show that social modularity effects are of paramount importance for designing successful, cost-effective advertising campaigns.

Social Network Status and Depression among Adolescents: An Examination of Social Network Influences and Depressive Symptoms in a Chinese Sample

PubMed Central

Okamoto, Janet; Johnson, C. Anderson; Leventhal, Adam; Milam, Joel; Pentz, Mary Ann; Schwartz, David; Valente, Thomas W.

2012-01-01

Despite the well established influence of peer experiences on adolescent attitudes, thoughts, and behaviors, surprisingly little research has examined the importance of peer context and the increased prevalence of depressive symptoms accompanying the transition into adolescence. Examination of social networks may provide some insight into the role of peers in the vulnerability of some adolescents to depression. To address this issue, we leveraged an existing sample of 5,563 Chinese 10th graders to incorporate social network data into a multilevel regression model of depressive symptoms. We found that, in this sample, being nominated as a friend was more important than being nominated as most liked. Social network centrality was significantly associated with depression; those adolescents who were less connected were more likely to suffer from depression. The risk of depression for those who were marginal members of classroom social networks was substantial. These findings suggest that a social network perspective could help to increase the effectiveness of programs aimed at preventing adolescent depression. PMID:23226988

Theoretical approaches of online social network interventions and implications for behavioral change: a systematic review.

PubMed

Arguel, Amaël; Perez-Concha, Oscar; Li, Simon Y W; Lau, Annie Y S

2018-02-01

The aim of this review was to identify general theoretical frameworks used in online social network interventions for behavioral change. To address this research question, a PRISMA-compliant systematic review was conducted. A systematic review (PROSPERO registration number CRD42014007555) was conducted using 3 electronic databases (PsycINFO, Pubmed, and Embase). Four reviewers screened 1788 abstracts. 15 studies were selected according to the eligibility criteria. Randomized controlled trials and controlled studies were assessed using Cochrane Collaboration's "risk-of-bias" tool, and narrative synthesis. Five eligible articles used the social cognitive theory as a framework to develop interventions targeting behavioral change. Other theoretical frameworks were related to the dynamics of social networks, intention models, and community engagement theories. Only one of the studies selected in the review mentioned a well-known theory from the field of health psychology. Conclusions were that guidelines are lacking in the design of online social network interventions for behavioral change. Existing theories and models from health psychology that are traditionally used for in situ behavioral change should be considered when designing online social network interventions in a health care setting. © 2016 John Wiley & Sons, Ltd.

Studies of Opinion Stability for Small Dynamic Networks with Opportunistic Agents

NASA Astrophysics Data System (ADS)

Sobkowicz, Pawel

There are numerous examples of societies with extremely stable mix of contrasting opinions. We argue that this stability is a result of an interplay between society network topology adjustment and opinion changing processes. To support this position we present a computer model of opinion formation based on some novel assumptions, designed to bring the model closer to social reality. In our model, the agents, in addition to changing their opinions due to influence of the rest of society and external propaganda, have the ability to modify their social network, forming links with agents sharing the same opinions and cutting the links with those they disagree with. To improve the model further we divide the agents into "fanatics" and "opportunists," depending on how easy it is to change their opinions. The simulations show significant differences compared to traditional models, where network links are static. In particular, for the dynamical model where inter-agent links are adjustable, the final network structure and opinion distribution is shown to resemble real world observations, such as social structures and persistence of minority groups even when most of the society is against them and the propaganda is strong.

A new similarity measure for link prediction based on local structures in social networks

NASA Astrophysics Data System (ADS)

Aghabozorgi, Farshad; Khayyambashi, Mohammad Reza

2018-07-01

Link prediction is a fundamental problem in social network analysis. There exist a variety of techniques for link prediction which applies the similarity measures to estimate proximity of vertices in the network. Complex networks like social networks contain structural units named network motifs. In this study, a newly developed similarity measure is proposed where these structural units are applied as the source of similarity estimation. This similarity measure is tested through a supervised learning experiment framework, where other similarity measures are compared with this similarity measure. The classification model trained with this similarity measure outperforms others of its kind.

The data-driven null models for information dissemination tree in social networks

NASA Astrophysics Data System (ADS)

Zhang, Zhiwei; Wang, Zhenyu

2017-10-01

For the purpose of detecting relatedness and co-occurrence between users, as well as the distribution features of nodes in spreading path of a social network, this paper explores topological characteristics of information dissemination trees (IDT) that can be employed indirectly to probe the information dissemination laws within social networks. Hence, three different null models of IDT are presented in this article, including the statistical-constrained 0-order IDT null model, the random-rewire-broken-edge 0-order IDT null model and the random-rewire-broken-edge 2-order IDT null model. These null models firstly generate the corresponding randomized copy of an actual IDT; then the extended significance profile, which is developed by adding the cascade ratio of information dissemination path, is exploited not only to evaluate degree correlation of two nodes associated with an edge, but also to assess the cascade ratio of different length of information dissemination paths. The experimental correspondences of the empirical analysis for several SinaWeibo IDTs and Twitter IDTs indicate that the IDT null models presented in this paper perform well in terms of degree correlation of nodes and dissemination path cascade ratio, which can be better to reveal the features of information dissemination and to fit the situation of real social networks.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure

NASA Astrophysics Data System (ADS)

Schleussner, Carl-Friedrich; Donges, Jonathan F.; Engemann, Denis A.; Levermann, Anders

2016-08-01

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure.

PubMed

Schleussner, Carl-Friedrich; Donges, Jonathan F; Engemann, Denis A; Levermann, Anders

2016-08-11

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.

Bayesian Analysis for Exponential Random Graph Models Using the Adaptive Exchange Sampler.

PubMed

Jin, Ick Hoon; Yuan, Ying; Liang, Faming

2013-10-01

Exponential random graph models have been widely used in social network analysis. However, these models are extremely difficult to handle from a statistical viewpoint, because of the intractable normalizing constant and model degeneracy. In this paper, we consider a fully Bayesian analysis for exponential random graph models using the adaptive exchange sampler, which solves the intractable normalizing constant and model degeneracy issues encountered in Markov chain Monte Carlo (MCMC) simulations. The adaptive exchange sampler can be viewed as a MCMC extension of the exchange algorithm, and it generates auxiliary networks via an importance sampling procedure from an auxiliary Markov chain running in parallel. The convergence of this algorithm is established under mild conditions. The adaptive exchange sampler is illustrated using a few social networks, including the Florentine business network, molecule synthetic network, and dolphins network. The results indicate that the adaptive exchange algorithm can produce more accurate estimates than approximate exchange algorithms, while maintaining the same computational efficiency.

Effect of socio-economic status, family smoking and mental health through social network on the substance use potential in adolescents: a mediation analysis.

PubMed

Sajjadi, H; Jorjoran Shushtari, Z; Mahboubi, S; Rafiey, H; Salimi, Y

2018-04-01

Understanding pathways that influence substance use potential (SUP) can help with effective substance use prevention interventions among adolescents. The aim of the present study is to contribute to a better understanding of the SUP of adolescents by examining the mediating role of social network quality in the SUP of Iranian adolescents. A cross-sectional study. Structural equation modeling was conducted to assess the hypothesized model that social network quality would mediate the association of family socio-economic status, a mental health disorder, and family smoking with addiction potential. The model shows a good fit to the data. Social network quality mediated the effect of family smoking on the SUP for boys. A mental health disorder had a positive significant direct effect on addiction potential for both girls and boys. Social network quality mediates the effect of family smoking on boys' addiction potential in the context of Iran. Educational programs based on local societal ways and cultural norms are recommended to change tobacco smoking behavior among family members. In addition, to prevent subsequent substance use among adolescents, more effort is needed to improve their mental health. Copyright © 2018 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Social contagions on time-varying community networks

NASA Astrophysics Data System (ADS)

Liu, Mian-Xin; Wang, Wei; Liu, Ying; Tang, Ming; Cai, Shi-Min; Zhang, Hai-Feng

2017-05-01

Time-varying community structures exist widely in real-world networks. However, previous studies on the dynamics of spreading seldom took this characteristic into account, especially those on social contagions. To study the effects of time-varying community structures on social contagions, we propose a non-Markovian social contagion model on time-varying community networks based on the activity-driven network model. A mean-field theory is developed to analyze the proposed model. Through theoretical analyses and numerical simulations, two hierarchical features of the behavior adoption processes are found. That is, when community strength is relatively large, the behavior can easily spread in one of the communities, while in the other community the spreading only occurs at higher behavioral information transmission rates. Meanwhile, in spatial-temporal evolution processes, hierarchical orders are observed for the behavior adoption. Moreover, under different information transmission rates, three distinctive patterns are demonstrated in the change of the whole network's final adoption proportion along with the growing community strength. Within a suitable range of transmission rate, an optimal community strength can be found that can maximize the final adoption proportion. Finally, compared with the average activity potential, the promoting or inhibiting of social contagions is much more influenced by the number of edges generated by active nodes.

Korean American dementia caregivers' attitudes toward caregiving: the role of social network versus satisfaction with social support.

PubMed

Lee, Youjung; Choi, Sunha

2013-06-01

The purpose of this study was to explore how Korean American family caregivers view the services they offer to patients with dementia. It also investigated the roles of social networks and satisfaction with social support on attitudes toward caregiving. Social network, satisfaction with social support, demographic characteristics, caregiving-related stress factors, and cultural factors were examined. We used a convenience sample of 85 Korean American dementia caregivers. The results from hierarchical multiple regression models show that the level of satisfaction with social support significantly contributed to Korean American caregivers' attitudes toward working with patients with dementia, while no statistically significant associate was found for social network. Higher levels of satisfaction with social support were associated with greater positive attitudes toward caregiving among Korean American caregivers (b = 0.26, p = .024). The implications for mental health professionals and policy makers are discussed.

Two-population dynamics in a growing network model

NASA Astrophysics Data System (ADS)

Ivanova, Kristinka; Iordanov, Ivan

2012-02-01

We introduce a growing network evolution model with nodal attributes. The model describes the interactions between potentially violent V and non-violent N agents who have different affinities in establishing connections within their own population versus between the populations. The model is able to generate all stable triads observed in real social systems. In the framework of rate equations theory, we employ the mean-field approximation to derive analytical expressions of the degree distribution and the local clustering coefficient for each type of nodes. Analytical derivations agree well with numerical simulation results. The assortativity of the potentially violent network qualitatively resembles the connectivity pattern in terrorist networks that was recently reported. The assortativity of the network driven by aggression shows clearly different behavior than the assortativity of the networks with connections of non-aggressive nature in agreement with recent empirical results of an online social system.

Integrating Entropy and Closed Frequent Pattern Mining for Social Network Modelling and Analysis

NASA Astrophysics Data System (ADS)

Adnan, Muhaimenul; Alhajj, Reda; Rokne, Jon

The recent increase in the explicitly available social networks has attracted the attention of the research community to investigate how it would be possible to benefit from such a powerful model in producing effective solutions for problems in other domains where the social network is implicit; we argue that social networks do exist around us but the key issue is how to realize and analyze them. This chapter presents a novel approach for constructing a social network model by an integrated framework that first preparing the data to be analyzed and then applies entropy and frequent closed patterns mining for network construction. For a given problem, we first prepare the data by identifying items and transactions, which arc the basic ingredients for frequent closed patterns mining. Items arc main objects in the problem and a transaction is a set of items that could exist together at one time (e.g., items purchased in one visit to the supermarket). Transactions could be analyzed to discover frequent closed patterns using any of the well-known techniques. Frequent closed patterns have the advantage that they successfully grab the inherent information content of the dataset and is applicable to a broader set of domains. Entropies of the frequent closed patterns arc used to keep the dimensionality of the feature vectors to a reasonable size; it is a kind of feature reduction process. Finally, we analyze the dynamic behavior of the constructed social network. Experiments were conducted on a synthetic dataset and on the Enron corpus email dataset. The results presented in the chapter show that social networks extracted from a feature set as frequent closed patterns successfully carry the community structure information. Moreover, for the Enron email dataset, we present an analysis to dynamically indicate the deviations from each user's individual and community profile. These indications of deviations can be very useful to identify unusual events.

Hierarchical Winner-Take-All Particle Swarm Optimization Social Network for Neural Model Fitting

PubMed Central

Coventry, Brandon S.; Parthasarathy, Aravindakshan; Sommer, Alexandra L.; Bartlett, Edward L.

2016-01-01

Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models. PMID:27726048

Seasonal Influenza Vaccination amongst Medical Students: A Social Network Analysis Based on a Cross-Sectional Study

PubMed Central

Edge, Rhiannon; Heath, Joseph; Rowlingson, Barry; Keegan, Thomas J.; Isba, Rachel

2015-01-01

Introduction The Chief Medical Officer for England recommends that healthcare workers have a seasonal influenza vaccination in an attempt to protect both patients and NHS staff. Despite this, many healthcare workers do not have a seasonal influenza vaccination. Social network analysis is a well-established research approach that looks at individuals in the context of their social connections. We examine the effects of social networks on influenza vaccination decision and disease dynamics. Methods We used a social network analysis approach to look at vaccination distribution within the network of the Lancaster Medical School students and combined these data with the students’ beliefs about vaccination behaviours. We then developed a model which simulated influenza outbreaks to study the effects of preferentially vaccinating individuals within this network. Results Of the 253 eligible students, 217 (86%) provided relational data, and 65% of responders had received a seasonal influenza vaccination. Students who were vaccinated were more likely to think other medical students were vaccinated. However, there was no clustering of vaccinated individuals within the medical student social network. The influenza simulation model demonstrated that vaccination of well-connected individuals may have a disproportional effect on disease dynamics. Conclusions This medical student population exhibited vaccination coverage levels similar to those seen in other healthcare groups but below recommendations. However, in this population, a lack of vaccination clustering might provide natural protection from influenza outbreaks. An individual student’s perception of the vaccination coverage amongst their peers appears to correlate with their own decision to vaccinate, but the directionality of this relationship is not clear. When looking at the spread of disease within a population it is important to include social structures alongside vaccination data. Social networks influence disease epidemiology and vaccination campaigns designed with information from social networks could be a future target for policy makers. PMID:26452223

Seasonal Influenza Vaccination amongst Medical Students: A Social Network Analysis Based on a Cross-Sectional Study.

PubMed

Edge, Rhiannon; Heath, Joseph; Rowlingson, Barry; Keegan, Thomas J; Isba, Rachel

2015-01-01

The Chief Medical Officer for England recommends that healthcare workers have a seasonal influenza vaccination in an attempt to protect both patients and NHS staff. Despite this, many healthcare workers do not have a seasonal influenza vaccination. Social network analysis is a well-established research approach that looks at individuals in the context of their social connections. We examine the effects of social networks on influenza vaccination decision and disease dynamics. We used a social network analysis approach to look at vaccination distribution within the network of the Lancaster Medical School students and combined these data with the students' beliefs about vaccination behaviours. We then developed a model which simulated influenza outbreaks to study the effects of preferentially vaccinating individuals within this network. Of the 253 eligible students, 217 (86%) provided relational data, and 65% of responders had received a seasonal influenza vaccination. Students who were vaccinated were more likely to think other medical students were vaccinated. However, there was no clustering of vaccinated individuals within the medical student social network. The influenza simulation model demonstrated that vaccination of well-connected individuals may have a disproportional effect on disease dynamics. This medical student population exhibited vaccination coverage levels similar to those seen in other healthcare groups but below recommendations. However, in this population, a lack of vaccination clustering might provide natural protection from influenza outbreaks. An individual student's perception of the vaccination coverage amongst their peers appears to correlate with their own decision to vaccinate, but the directionality of this relationship is not clear. When looking at the spread of disease within a population it is important to include social structures alongside vaccination data. Social networks influence disease epidemiology and vaccination campaigns designed with information from social networks could be a future target for policy makers.

Intention to continue using Facebook fan pages from the perspective of social capital theory.

PubMed

Lin, Kuan-Yu; Lu, Hsi-Peng

2011-10-01

Social network sites enable users to express themselves, establish ties, and develop and maintain social relationships. Recently, many companies have begun using social media identity (e.g., Facebook fan pages) to enhance brand attractiveness, and social network sites have evolved into social utility networks, thereby creating a number of promising business opportunities. To this end, the operators of fan pages need to be aware of the factors motivating users to continue their patronization of such pages. This study set out to identify these motivating factors from the point of view of social capital. This study employed structural equation modeling to investigate a research model based on a survey of 327 fan pages users. This study discovered that ties related to social interaction (structural dimension), shared values (cognitive dimension), and trust (relational dimension) play important roles in users' continued intention to use Facebook fan pages. Finally, this study discusses the implications of these findings and offers directions for future research.

An actor-based model of social network influence on adolescent body size, screen time, and playing sports.

PubMed

Shoham, David A; Tong, Liping; Lamberson, Peter J; Auchincloss, Amy H; Zhang, Jun; Dugas, Lara; Kaufman, Jay S; Cooper, Richard S; Luke, Amy

2012-01-01

Recent studies suggest that obesity may be "contagious" between individuals in social networks. Social contagion (influence), however, may not be identifiable using traditional statistical approaches because they cannot distinguish contagion from homophily (the propensity for individuals to select friends who are similar to themselves) or from shared environmental influences. In this paper, we apply the stochastic actor-based model (SABM) framework developed by Snijders and colleagues to data on adolescent body mass index (BMI), screen time, and playing active sports. Our primary hypothesis was that social influences on adolescent body size and related behaviors are independent of friend selection. Employing the SABM, we simultaneously modeled network dynamics (friendship selection based on homophily and structural characteristics of the network) and social influence. We focused on the 2 largest schools in the National Longitudinal Study of Adolescent Health (Add Health) and held the school environment constant by examining the 2 school networks separately (N = 624 and 1151). Results show support in both schools for homophily on BMI, but also for social influence on BMI. There was no evidence of homophily on screen time in either school, while only one of the schools showed homophily on playing active sports. There was, however, evidence of social influence on screen time in one of the schools, and playing active sports in both schools. These results suggest that both homophily and social influence are important in understanding patterns of adolescent obesity. Intervention efforts should take into consideration peers' influence on one another, rather than treating "high risk" adolescents in isolation.

A Markov chain model for image ranking system in social networks

NASA Astrophysics Data System (ADS)

Zin, Thi Thi; Tin, Pyke; Toriu, Takashi; Hama, Hiromitsu

2014-03-01

In today world, different kinds of networks such as social, technological, business and etc. exist. All of the networks are similar in terms of distributions, continuously growing and expanding in large scale. Among them, many social networks such as Facebook, Twitter, Flickr and many others provides a powerful abstraction of the structure and dynamics of diverse kinds of inter personal connection and interaction. Generally, the social network contents are created and consumed by the influences of all different social navigation paths that lead to the contents. Therefore, identifying important and user relevant refined structures such as visual information or communities become major factors in modern decision making world. Moreover, the traditional method of information ranking systems cannot be successful due to their lack of taking into account the properties of navigation paths driven by social connections. In this paper, we propose a novel image ranking system in social networks by using the social data relational graphs from social media platform jointly with visual data to improve the relevance between returned images and user intentions (i.e., social relevance). Specifically, we propose a Markov chain based Social-Visual Ranking algorithm by taking social relevance into account. By using some extensive experiments, we demonstrated the significant and effectiveness of the proposed social-visual ranking method.

Peer-Learning Networks in Social Work Doctoral Education: An Interdisciplinary Model

ERIC Educational Resources Information Center

Miller, J. Jay; Duron, Jacquelynn F.; Bosk, Emily Adlin; Finno-Velasquez, Megan; Abner, Kristin S.

2016-01-01

Peer-learning networks (PLN) can be valuable tools for doctoral students. Participation in these networks can aid in the completion of the dissertation, lead to increased scholarship productivity, and assist in student retention. Yet, despite the promise of PLNs, few studies have documented their effect on social work doctoral education. This…

Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation

PubMed Central

Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

2015-01-01

Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency. PMID:26609303

Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation.

PubMed

Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

2015-01-01

Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.

A Model of Biological Attacks on a Realistic Population

NASA Astrophysics Data System (ADS)

Carley, Kathleen M.; Fridsma, Douglas; Casman, Elizabeth; Altman, Neal; Chen, Li-Chiou; Kaminsky, Boris; Nave, Demian; Yahja, Alex

The capability to assess the impacts of large-scale biological attacks and the efficacy of containment policies is critical and requires knowledge-intensive reasoning about social response and disease transmission within a complex social system. There is a close linkage among social networks, transportation networks, disease spread, and early detection. Spatial dimensions related to public gathering places such as hospitals, nursing homes, and restaurants, can play a major role in epidemics [Klovdahl et. al. 2001]. Like natural epidemics, bioterrorist attacks unfold within spatially defined, complex social systems, and the societal and networked response can have profound effects on their outcome. This paper focuses on bioterrorist attacks, but the model has been applied to emergent and familiar diseases as well.

Random walks on activity-driven networks with attractiveness

NASA Astrophysics Data System (ADS)

Alessandretti, Laura; Sun, Kaiyuan; Baronchelli, Andrea; Perra, Nicola

2017-05-01

Virtually all real-world networks are dynamical entities. In social networks, the propensity of nodes to engage in social interactions (activity) and their chances to be selected by active nodes (attractiveness) are heterogeneously distributed. Here, we present a time-varying network model where each node and the dynamical formation of ties are characterized by these two features. We study how these properties affect random-walk processes unfolding on the network when the time scales describing the process and the network evolution are comparable. We derive analytical solutions for the stationary state and the mean first-passage time of the process, and we study cases informed by empirical observations of social networks. Our work shows that previously disregarded properties of real social systems, such as heterogeneous distributions of activity and attractiveness as well as the correlations between them, substantially affect the dynamical process unfolding on the network.

Network characteristics, perceived social support, and psychological adjustment in mothers of children with autism spectrum disorder.

PubMed

Benson, Paul R

2012-12-01

This study examined the characteristics of the support networks of 106 mothers of children with ASD and their relationship to perceived social support, depressed mood, and subjective well-being. Using structural equation modeling, two competing sets of hypotheses were assessed: (1) that network characteristics would impact psychological adjustment directly, and (2) that network effects on adjustment would be indirect, mediated by perceived social support. Results primarily lent support to the latter hypotheses, with measures of network structure (network size) and function (proportion of network members providing emotional support) predicting increased levels of perceived social support which, in turn, predicted decreased depressed mood and increased well-being. Results also indicated that increased interpersonal strain in the maternal network was directly and indirectly associated with increased maternal depression, while being indirectly linked to reduced well-being. Study limitations and implications are discussed.

Memory Transmission in Small Groups and Large Networks: An Agent-Based Model.

PubMed

Luhmann, Christian C; Rajaram, Suparna

2015-12-01

The spread of social influence in large social networks has long been an interest of social scientists. In the domain of memory, collaborative memory experiments have illuminated cognitive mechanisms that allow information to be transmitted between interacting individuals, but these experiments have focused on small-scale social contexts. In the current study, we took a computational approach, circumventing the practical constraints of laboratory paradigms and providing novel results at scales unreachable by laboratory methodologies. Our model embodied theoretical knowledge derived from small-group experiments and replicated foundational results regarding collaborative inhibition and memory convergence in small groups. Ultimately, we investigated large-scale, realistic social networks and found that agents are influenced by the agents with which they interact, but we also found that agents are influenced by nonneighbors (i.e., the neighbors of their neighbors). The similarity between these results and the reports of behavioral transmission in large networks offers a major theoretical insight by linking behavioral transmission to the spread of information. © The Author(s) 2015.

Reconstruction of a Real World Social Network using the Potts Model and Loopy Belief Propagation.

PubMed

Bisconti, Cristian; Corallo, Angelo; Fortunato, Laura; Gentile, Antonio A; Massafra, Andrea; Pellè, Piergiuseppe

2015-01-01

The scope of this paper is to test the adoption of a statistical model derived from Condensed Matter Physics, for the reconstruction of the structure of a social network. The inverse Potts model, traditionally applied to recursive observations of quantum states in an ensemble of particles, is here addressed to observations of the members' states in an organization and their (anti)correlations, thus inferring interactions as links among the members. Adopting proper (Bethe) approximations, such an inverse problem is showed to be tractable. Within an operational framework, this network-reconstruction method is tested for a small real-world social network, the Italian parliament. In this study case, it is easy to track statuses of the parliament members, using (co)sponsorships of law proposals as the initial dataset. In previous studies of similar activity-based networks, the graph structure was inferred directly from activity co-occurrences: here we compare our statistical reconstruction with such standard methods, outlining discrepancies and advantages.

Reconstruction of a Real World Social Network using the Potts Model and Loopy Belief Propagation

PubMed Central

Bisconti, Cristian; Corallo, Angelo; Fortunato, Laura; Gentile, Antonio A.; Massafra, Andrea; Pellè, Piergiuseppe

2015-01-01

The scope of this paper is to test the adoption of a statistical model derived from Condensed Matter Physics, for the reconstruction of the structure of a social network. The inverse Potts model, traditionally applied to recursive observations of quantum states in an ensemble of particles, is here addressed to observations of the members' states in an organization and their (anti)correlations, thus inferring interactions as links among the members. Adopting proper (Bethe) approximations, such an inverse problem is showed to be tractable. Within an operational framework, this network-reconstruction method is tested for a small real-world social network, the Italian parliament. In this study case, it is easy to track statuses of the parliament members, using (co)sponsorships of law proposals as the initial dataset. In previous studies of similar activity-based networks, the graph structure was inferred directly from activity co-occurrences: here we compare our statistical reconstruction with such standard methods, outlining discrepancies and advantages. PMID:26617539

Complex social contagion makes networks more vulnerable to disease outbreaks.

PubMed

Campbell, Ellsworth; Salathé, Marcel

2013-01-01

Social network analysis is now widely used to investigate the dynamics of infectious disease spread. Vaccination dramatically disrupts disease transmission on a contact network, and indeed, high vaccination rates can potentially halt disease transmission altogether. Here, we build on mounting evidence that health behaviors - such as vaccination, and refusal thereof - can spread across social networks through a process of complex contagion that requires social reinforcement. Using network simulations that model health behavior and infectious disease spread, we find that under otherwise identical conditions, the process by which the health behavior spreads has a very strong effect on disease outbreak dynamics. This dynamic variability results from differences in the topology within susceptible communities that arise during the health behavior spreading process, which in turn depends on the topology of the overall social network. Our findings point to the importance of health behavior spread in predicting and controlling disease outbreaks.

Social network analysis for program implementation.

PubMed

Valente, Thomas W; Palinkas, Lawrence A; Czaja, Sara; Chu, Kar-Hai; Brown, C Hendricks

2015-01-01

This paper introduces the use of social network analysis theory and tools for implementation research. The social network perspective is useful for understanding, monitoring, influencing, or evaluating the implementation process when programs, policies, practices, or principles are designed and scaled up or adapted to different settings. We briefly describe common barriers to implementation success and relate them to the social networks of implementation stakeholders. We introduce a few simple measures commonly used in social network analysis and discuss how these measures can be used in program implementation. Using the four stage model of program implementation (exploration, adoption, implementation, and sustainment) proposed by Aarons and colleagues [1] and our experience in developing multi-sector partnerships involving community leaders, organizations, practitioners, and researchers, we show how network measures can be used at each stage to monitor, intervene, and improve the implementation process. Examples are provided to illustrate these concepts. We conclude with expected benefits and challenges associated with this approach.

Social Network Analysis for Program Implementation

PubMed Central

Valente, Thomas W.; Palinkas, Lawrence A.; Czaja, Sara; Chu, Kar-Hai; Brown, C. Hendricks

2015-01-01

This paper introduces the use of social network analysis theory and tools for implementation research. The social network perspective is useful for understanding, monitoring, influencing, or evaluating the implementation process when programs, policies, practices, or principles are designed and scaled up or adapted to different settings. We briefly describe common barriers to implementation success and relate them to the social networks of implementation stakeholders. We introduce a few simple measures commonly used in social network analysis and discuss how these measures can be used in program implementation. Using the four stage model of program implementation (exploration, adoption, implementation, and sustainment) proposed by Aarons and colleagues [1] and our experience in developing multi-sector partnerships involving community leaders, organizations, practitioners, and researchers, we show how network measures can be used at each stage to monitor, intervene, and improve the implementation process. Examples are provided to illustrate these concepts. We conclude with expected benefits and challenges associated with this approach. PMID:26110842

Social networks and alcohol use among older adults: a comparison with middle-aged adults.

PubMed

Kim, Seungyoun; Spilman, Samantha L; Liao, Diana H; Sacco, Paul; Moore, Alison A

2018-04-01

This study compared the association between social networks and alcohol consumption among middle-aged (MA) and older adults (OA) to better understand the nature of the relationship between those two factors among OA and MA. We examined Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Current drinkers aged over 50 were subdivided into two age groups: MA (50-64, n = 5214) and OA (65 and older, n = 3070). Each age group was stratified into drinking levels (low-risk vs. at-risk) based on alcohol consumption. The size and diversity of social networks were measured. Logistic regression models were used to examine age differences in the association between the social networks (size and diversity) and the probability of at-risk drinking among two age groups. A significant association between the social networks diversity and lower odds of at-risk drinking was found among MA and OA. However, the relationship between the diversity of social networks and the likelihood of at-risk drinking was weaker for OA than for MA. The association between social networks size and at-risk drinking was not significant among MA and OA. The current study suggests that the association between social networks diversity and alcohol use among OA differs from the association among MA, and few social networks were associated with alcohol use among OA. In the future, research should consider an in-depth exploration of the nature of social networks and alcohol consumption by using longitudinal designs and advanced methods of exploring drinking networks.

Social networks and alcohol use among older adults: a comparison with middle-aged adults

PubMed Central

Kim, Seungyoun; Spilman, Samantha L.; Liao, Diana H.; Sacco, Paul; Moore, Alison A.

2017-01-01

Objectives This study compared the association between social networks and alcohol consumption among middle-aged (MA) and older adults (OA) to better understand the nature of the relationship between those two factors among OA and MA. Method We examined Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Current drinkers aged over 50 were subdivided into two age groups: MA (50–64, n = 5214) and OA (65 and older, n = 3070). Each age group was stratified into drinking levels (low-risk vs. at-risk) based on alcohol consumption. The size and diversity of social networks were measured. Logistic regression models were used to examine age differences in the association between the social networks (size and diversity) and the probability of at-risk drinking among two age groups. Results A significant association between the social networks diversity and lower odds of at-risk drinking was found among MA and OA. However, the relationship between the diversity of social networks and the likelihood of at-risk drinking was weaker for OA than for MA. The association between social networks size and at-risk drinking was not significant among MA and OA. Conclusion The current study suggests that the association between social networks diversity and alcohol use among OA differs from the association among MA, and few social networks were associated with alcohol use among OA. In the future, research should consider an in-depth exploration of the nature of social networks and alcohol consumption by using longitudinal designs and advanced methods of exploring drinking networks. PMID:28006983

Dynamics of bounded confidence opinion in heterogeneous social networks: Concord against partial antagonism

NASA Astrophysics Data System (ADS)

Kurmyshev, Evguenii; Juárez, Héctor A.; González-Silva, Ricardo A.

2011-08-01

Bounded confidence models of opinion dynamics in social networks have been actively studied in recent years, in particular, opinion formation and extremism propagation along with other aspects of social dynamics. In this work, after an analysis of limitations of the Deffuant-Weisbuch (DW) bounded confidence, relative agreement model, we propose the mixed model that takes into account two psychological types of individuals. Concord agents (C-agents) are friendly people; they interact in a way that their opinions always get closer. Agents of the other psychological type show partial antagonism in their interaction (PA-agents). Opinion dynamics in heterogeneous social groups, consisting of agents of the two types, was studied on different social networks: Erdös-Rényi random graphs, small-world networks and complete graphs. Limit cases of the mixed model, pure C- and PA-societies, were also studied. We found that group opinion formation is, qualitatively, almost independent of the topology of networks used in this work. Opinion fragmentation, polarization and consensus are observed in the mixed model at different proportions of PA- and C-agents, depending on the value of initial opinion tolerance of agents. As for the opinion formation and arising of “dissidents”, the opinion dynamics of the C-agents society was found to be similar to that of the DW model, except for the rate of opinion convergence. Nevertheless, mixed societies showed dynamics and bifurcation patterns notably different to those of the DW model. The influence of biased initial conditions over opinion formation in heterogeneous social groups was also studied versus the initial value of opinion uncertainty, varying the proportion of the PA- to C-agents. Bifurcation diagrams showed an impressive evolution of collective opinion, in particular, radical changes of left to right consensus or vice versa at an opinion uncertainty value equal to 0.7 in the model with the PA/C mixture of population near 50/50.

Awareness Through Agility: Teenagers as a Model for Terrorist Development of Situational Awareness

DTIC Science & Technology

2006-05-01

relationship. Two of the most popular social networking sites are Myspace.com and Facebook.com. MySpace is currently the world’s...blogs allows teenagers to express themselves and, in some cases, gain limited notoriety. Blogs differ from web-based social networking sites as the...posted to web-based social networking sites , blogs, or instant messaging profiles; pictures that are taken out of context can be sent via cell

Leveraging social networks for understanding the evolution of epidemics

PubMed Central

2011-01-01

Background To understand how infectious agents disseminate throughout a population it is essential to capture the social model in a realistic manner. This paper presents a novel approach to modeling the propagation of the influenza virus throughout a realistic interconnection network based on actual individual interactions which we extract from online social networks. The advantage is that these networks can be extracted from existing sources which faithfully record interactions between people in their natural environment. We additionally allow modeling the characteristics of each individual as well as customizing his daily interaction patterns by making them time-dependent. Our purpose is to understand how the infection spreads depending on the structure of the contact network and the individuals who introduce the infection in the population. This would help public health authorities to respond more efficiently to epidemics. Results We implement a scalable, fully distributed simulator and validate the epidemic model by comparing the simulation results against the data in the 2004-2005 New York State Department of Health Report (NYSDOH), with similar temporal distribution results for the number of infected individuals. We analyze the impact of different types of connection models on the virus propagation. Lastly, we analyze and compare the effects of adopting several different vaccination policies, some of them based on individual characteristics -such as age- while others targeting the super-connectors in the social model. Conclusions This paper presents an approach to modeling the propagation of the influenza virus via a realistic social model based on actual individual interactions extracted from online social networks. We implemented a scalable, fully distributed simulator and we analyzed both the dissemination of the infection and the effect of different vaccination policies on the progress of the epidemics. The epidemic values predicted by our simulator match real data from NYSDOH. Our results show that our simulator can be a useful tool in understanding the differences in the evolution of an epidemic within populations with different characteristics and can provide guidance with regard to which, and how many, individuals should be vaccinated to slow down the virus propagation and reduce the number of infections. PMID:22784620

Dynamics of Opinion Forming in Structurally Balanced Social Networks

PubMed Central

Altafini, Claudio

2012-01-01

A structurally balanced social network is a social community that splits into two antagonistic factions (typical example being a two-party political system). The process of opinion forming on such a community is most often highly predictable, with polarized opinions reflecting the bipartition of the network. The aim of this paper is to suggest a class of dynamical systems, called monotone systems, as natural models for the dynamics of opinion forming on structurally balanced social networks. The high predictability of the outcome of a decision process is explained in terms of the order-preserving character of the solutions of this class of dynamical systems. If we represent a social network as a signed graph in which individuals are the nodes and the signs of the edges represent friendly or hostile relationships, then the property of structural balance corresponds to the social community being splittable into two antagonistic factions, each containing only friends. PMID:22761667

Simulating Social Networks of Online Communities: Simulation as a Method for Sociability Design

NASA Astrophysics Data System (ADS)

Ang, Chee Siang; Zaphiris, Panayiotis

We propose the use of social simulations to study and support the design of online communities. In this paper, we developed an Agent-Based Model (ABM) to simulate and study the formation of social networks in a Massively Multiplayer Online Role Playing Game (MMORPG) guild community. We first analyzed the activities and the social network (who-interacts-with-whom) of an existing guild community to identify its interaction patterns and characteristics. Then, based on the empirical results, we derived and formalized the interaction rules, which were implemented in our simulation. Using the simulation, we reproduced the observed social network of the guild community as a means of validation. The simulation was then used to examine how various parameters of the community (e.g. the level of activity, the number of neighbors of each agent, etc) could potentially influence the characteristic of the social networks.

Animal models of social stress: the dark side of social interactions.

PubMed

Masis-Calvo, Marianela; Schmidtner, Anna K; de Moura Oliveira, Vinícius E; Grossmann, Cindy P; de Jong, Trynke R; Neumann, Inga D

2018-05-10

Social stress occurs in all social species, including humans, and shape both mental health and future interactions with conspecifics. Animal models of social stress are used to unravel the precise role of the main stress system - the HPA axis - on the one hand, and the social behavior network on the other, as these are intricately interwoven. The present review aims to summarize the insights gained from three highly useful and clinically relevant animal models of psychosocial stress: the resident-intruder (RI) test, the chronic subordinate colony housing (CSC), and the social fear conditioning (SFC). Each model brings its own focus: the role of the HPA axis in shaping acute social confrontations (RI test), the physiological and behavioral impairments resulting from chronic exposure to negative social experiences (CSC), and the neurobiology underlying social fear and its effects on future social interactions (SFC). Moreover, these models are discussed with special attention to the HPA axis and the neuropeptides vasopressin and oxytocin, which are important messengers in the stress system, in emotion regulation, as well as in the social behavior network. It appears that both nonapeptides balance the relative strength of the stress response, and simultaneously predispose the animal to positive or negative social interactions.

Emergence of encounter networks due to human mobility.

PubMed

Riascos, A P; Mateos, José L

2017-01-01

There is a burst of work on human mobility and encounter networks. However, the connection between these two important fields just begun recently. It is clear that both are closely related: Mobility generates encounters, and these encounters might give rise to contagion phenomena or even friendship. We model a set of random walkers that visit locations in space following a strategy akin to Lévy flights. We measure the encounters in space and time and establish a link between walkers after they coincide several times. This generates a temporal network that is characterized by global quantities. We compare this dynamics with real data for two cities: New York City and Tokyo. We use data from the location-based social network Foursquare and obtain the emergent temporal encounter network, for these two cities, that we compare with our model. We found long-range (Lévy-like) distributions for traveled distances and time intervals that characterize the emergent social network due to human mobility. Studying this connection is important for several fields like epidemics, social influence, voting, contagion models, behavioral adoption and diffusion of ideas.

Social networks dynamics revealed by temporal analysis: An example in a non-human primate (Macaca sylvanus) in "La Forêt des Singes".

PubMed

Sosa, Sebastian; Zhang, Peng; Cabanes, Guénaël

2017-06-01

This study applied a temporal social network analysis model to describe three affiliative social networks (allogrooming, sleep in contact, and triadic interaction) in a non-human primate species, Macaca sylvanus. Three main social mechanisms were examined to determine interactional patterns among group members, namely preferential attachment (i.e., highly connected individuals are more likely to form new connections), triadic closure (new connections occur via previous close connections), and homophily (individuals interact preferably with others with similar attributes). Preferential attachment was only observed for triadic interaction network. Triadic closure was significant in allogrooming and triadic interaction networks. Finally, gender homophily was seasonal for allogrooming and sleep in contact networks, and observed in each period for triadic interaction network. These individual-based behaviors are based on individual reactions, and their analysis can shed light on the formation of the affiliative networks determining ultimate coalition networks, and how these networks may evolve over time. A focus on individual behaviors is necessary for a global interactional approach to understanding social behavior rules and strategies. When combined, these social processes could make animal social networks more resilient, thus enabling them to face drastic environmental changes. This is the first study to pinpoint some of the processes underlying the formation of a social structure in a non-human primate species, and identify common mechanisms with humans. The approach used in this study provides an ideal tool for further research seeking to answer long-standing questions about social network dynamics. © 2017 Wiley Periodicals, Inc.

Characterizing and modeling the dynamics of activity and popularity.

PubMed

Zhang, Peng; Li, Menghui; Gao, Liang; Fan, Ying; Di, Zengru

2014-01-01

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks.

Characterizing and Modeling the Dynamics of Activity and Popularity

PubMed Central

Zhang, Peng; Li, Menghui; Gao, Liang; Fan, Ying; Di, Zengru

2014-01-01

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks. PMID:24586586

Usefulness of Neuro-Fuzzy Models' Application for Tobacco Control

NASA Astrophysics Data System (ADS)

Petrovic-Lazarevic, Sonja; Zhang, Jian Ying

2007-12-01

The paper presents neuro-fuzzy models' application appropriate for tobacco control: the fuzzy control model, Adaptive Network Based Fuzzy Inference System, Evolving Fuzzy Neural Network models, and EVOlving POLicies. We propose further the use of Fuzzy Casual Networks to help tobacco control decision makers develop policies and measure their impact on social regulation.

An Approach Based on Social Network Analysis Applied to a Collaborative Learning Experience

ERIC Educational Resources Information Center

Claros, Iván; Cobos, Ruth; Collazos, César A.

2016-01-01

The Social Network Analysis (SNA) techniques allow modelling and analysing the interaction among individuals based on their attributes and relationships. This approach has been used by several researchers in order to measure the social processes in collaborative learning experiences. But oftentimes such measures were calculated at the final state…

Emergence of Scale-Free Leadership Structure in Social Recommender Systems

PubMed Central

Zhou, Tao; Medo, Matúš; Cimini, Giulio; Zhang, Zi-Ke; Zhang, Yi-Cheng

2011-01-01

The study of the organization of social networks is important for the understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a “good get richer” mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems. PMID:21857891

A Mechanistic Model of Human Recall of Social Network Structure and Relationship Affect.

PubMed

Omodei, Elisa; Brashears, Matthew E; Arenas, Alex

2017-12-07

The social brain hypothesis argues that the need to deal with social challenges was key to our evolution of high intelligence. Research with non-human primates as well as experimental and fMRI studies in humans produce results consistent with this claim, leading to an estimate that human primary groups should consist of roughly 150 individuals. Gaps between this prediction and empirical observations can be partially accounted for using "compression heuristics", or schemata that simplify the encoding and recall of social information. However, little is known about the specific algorithmic processes used by humans to store and recall social information. We describe a mechanistic model of human network recall and demonstrate its sufficiency for capturing human recall behavior observed in experimental contexts. We find that human recall is predicated on accurate recall of a small number of high degree network nodes and the application of heuristics for both structural and affective information. This provides new insight into human memory, social network evolution, and demonstrates a novel approach to uncovering human cognitive operations.

Inferring Social Status and Rich Club Effects in Enterprise Communication Networks

PubMed Central

Dong, Yuxiao; Tang, Jie; Chawla, Nitesh V.; Lou, Tiancheng; Yang, Yang; Wang, Bai

2015-01-01

Social status, defined as the relative rank or position that an individual holds in a social hierarchy, is known to be among the most important motivating forces in social behaviors. In this paper, we consider the notion of status from the perspective of a position or title held by a person in an enterprise. We study the intersection of social status and social networks in an enterprise. We study whether enterprise communication logs can help reveal how social interactions and individual status manifest themselves in social networks. To that end, we use two enterprise datasets with three communication channels — voice call, short message, and email — to demonstrate the social-behavioral differences among individuals with different status. We have several interesting findings and based on these findings we also develop a model to predict social status. On the individual level, high-status individuals are more likely to be spanned as structural holes by linking to people in parts of the enterprise networks that are otherwise not well connected to one another. On the community level, the principle of homophily, social balance and clique theory generally indicate a “rich club” maintained by high-status individuals, in the sense that this community is much more connected, balanced and dense. Our model can predict social status of individuals with 93% accuracy. PMID:25822343

Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network

PubMed Central

Shen, Yelong; Phan, NhatHai; Xiao, Xiao; Jin, Ruoming; Sun, Junfeng; Piniewski, Brigitte; Kil, David; Dou, Dejing

2016-01-01

Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals' behaviors influenced by their friends' previous behaviors, SGP models the dynamic social correlation as the result of social influence. The SGP model naturally incorporates personal behavior factor and social correlation factor (i.e., the homophily principle: Friends tend to perform similar behaviors) into a unified model. And it models the social influence factor (i.e., an individual's behavior can be affected by his/her friends) implicitly in dynamic social correlation schemes. The detailed experimental evaluation has shown the SGP model achieves better prediction accuracy compared with most of baseline methods. However, a Socialized Random Forest model may perform better at the beginning compared with the SGP model. One of the main reasons is the dynamic social correlation function is purely based on the users' sequential behaviors without considering other physical activity-related features. To address this issue, we further propose a novel “multi-feature SGP model” (mfSGP) which improves the SGP model by using multiple physical activity-related features in the dynamic social correlation learning. Extensive experimental results illustrate that the mfSGP model clearly outperforms all other models in terms of prediction accuracy and running time. PMID:27746515

Dynamic Trust Models between Users over Social Networks

DTIC Science & Technology

2016-03-30

SUPPLEMENTARY NOTES 14. ABSTRACT In this project, by focusing on a number of word -of- mouth communication websites, we attempted to...analyzed evolution of trust networks in social media sites from a perspective of mediators. To this end, we proposed two stochastic models that...focusing on a number of word -of- mouth communication websites, we first attempt to construct dynamic trust models between users that enable to explain trust

Social significance of community structure: Statistical view

NASA Astrophysics Data System (ADS)

Li, Hui-Jia; Daniels, Jasmine J.

2015-01-01

Community structure analysis is a powerful tool for social networks that can simplify their topological and functional analysis considerably. However, since community detection methods have random factors and real social networks obtained from complex systems always contain error edges, evaluating the significance of a partitioned community structure is an urgent and important question. In this paper, integrating the specific characteristics of real society, we present a framework to analyze the significance of a social community. The dynamics of social interactions are modeled by identifying social leaders and corresponding hierarchical structures. Instead of a direct comparison with the average outcome of a random model, we compute the similarity of a given node with the leader by the number of common neighbors. To determine the membership vector, an efficient community detection algorithm is proposed based on the position of the nodes and their corresponding leaders. Then, using a log-likelihood score, the tightness of the community can be derived. Based on the distribution of community tightness, we establish a connection between p -value theory and network analysis, and then we obtain a significance measure of statistical form . Finally, the framework is applied to both benchmark networks and real social networks. Experimental results show that our work can be used in many fields, such as determining the optimal number of communities, analyzing the social significance of a given community, comparing the performance among various algorithms, etc.

Exploiting the Use of Social Networking to Facilitate Collaboration in the Scientific Community

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coppock, Edrick G.

The goal of this project was to exploit social networking to facilitate scientific collaboration. The project objective was to research and identify scientific collaboration styles that are best served by social networking applications and to model the most effective social networking applications to substantiate how social networking can support scientific collaboration. To achieve this goal and objective, the project was to develop an understanding of the types of collaborations conducted by scientific researchers, through classification, data analysis and identification of unique collaboration requirements. Another technical objective in support of this goal was to understand the current state of technology inmore » collaboration tools. In order to test hypotheses about which social networking applications effectively support scientific collaboration the project was to create a prototype scientific collaboration system. The ultimate goal for testing the hypotheses and research of the project was to refine the prototype into a functional application that could effectively facilitate and grow collaboration within the U.S. Department of Energy (DOE) research community.« less

Five-year trajectories of social networks and social support in older adults with major depression.

PubMed

Voils, Corrine I; Allaire, Jason C; Olsen, Maren K; Steffens, David C; Hoyle, Rick H; Bosworth, Hayden B

2007-12-01

Research with nondepressed adults suggests that social networks and social support are stable over the life course until very late age. This may not hold true for older adults with depression. We examined baseline status and trajectories of social networks and social support at the group and individual levels over five years. The sample consisted of 339 initially depressed adults aged 59 or older (M = 69 years) enrolled in a naturalistic study of depression. Measures of social ties, including social network size, frequency of interaction, instrumental support, and subjective support, were administered at baseline and yearly for five years. Latent growth curve models were estimated for each aspect of social ties. On average, social network size and frequency of interaction were low at baseline and remained stable over time, whereas subjective and instrumental support were high at baseline yet increased over time. There was significant variation in the direction and rate of change over time, which was not predicted by demographic or clinical factors. Because increasing social networks may be ineffective and may not be possible for a portion of people who already receive maximal support, interventions to increase social support may only work for a portion of older depressed adults.

Emergence of communities and diversity in social networks

PubMed Central

Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross

2017-01-01

Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics. PMID:28235785

Emergence of communities and diversity in social networks.

PubMed

Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross; Stanley, H Eugene

2017-03-14

Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics.

The Mediating Role of Loneliness in the Relation Between Social Engagement and Depressive Symptoms Among Older Korean Americans: Do Men and Women Differ?

PubMed Central

2013-01-01

Objectives. This study conceptualized loneliness as a mediator in the relation between social engagement and depressive symptoms and explored gender differences in the mediation model. Various indices of social engagement were considered including living arrangement, social network, and activity participation. Method. Using data from 674 community-dwelling Korean American older adults, we first examined the mediation effect of loneliness in the relation between each of 3 indices of social engagement (not living alone, social network, and activity participation) and depressive symptoms. Subsequently, gender differences in the mediation model were examined. Results. As hypothesized, loneliness was found to mediate the relation between each of the indices of social engagement and depressive symptoms in both men and women. We also observed gender differences in the strength of mediating effects; the effect of living alone was more likely to be mediated by loneliness among men, whereas women showed greater levels of mediation in the models with social network and activity participation. Discussion. Our findings suggest that loneliness may explain the mechanism by which deficits in social engagement exerts its effect on depressive symptoms and that gender differences should be considered in interventions targeting social engagement for mental health promotion. PMID:22929386

Social Networks and Welfare in Future Animal Management

PubMed Central

Koene, Paul; Ipema, Bert

2014-01-01

Simple Summary Living in a stable social environment is important to animals. Animal species have developed social behaviors and rules of approach and avoidance of conspecifics in order to co-exist. Animal species are kept or domesticated without explicit regard for their inherent social behavior and rules. Examples of social structures are provided for four species kept and managed by humans. This information is important for the welfare management of these species. In the near future, automatic measurement of social structures will provide a tool for daily welfare management together with nearest neighbor information. Abstract It may become advantageous to keep human-managed animals in the social network groups to which they have adapted. Data concerning the social networks of farm animal species and their ancestors are scarce but essential to establishing the importance of a natural social network for farmed animal species. Social Network Analysis (SNA) facilitates the characterization of social networking at group, subgroup and individual levels. SNA is currently used for modeling the social behavior and management of wild animals and social welfare of zoo animals. It has been recognized for use with farm animals but has yet to be applied for management purposes. Currently, the main focus is on cattle, because in large groups (poultry), recording of individuals is expensive and the existence of social networks is uncertain due to on-farm restrictions. However, in many cases, a stable social network might be important to individual animal fitness, survival and welfare. For instance, when laying hens are not too densely housed, simple networks may be established. We describe here small social networks in horses, brown bears, laying hens and veal calves to illustrate the importance of measuring social networks among animals managed by humans. Emphasis is placed on the automatic measurement of identity, location, nearest neighbors and nearest neighbor distance for management purposes. It is concluded that social networks are important to the welfare of human-managed animal species and that welfare management based on automatic recordings will become available in the near future. PMID:26479886

Infection dynamics on spatial small-world network models

NASA Astrophysics Data System (ADS)

Iotti, Bryan; Antonioni, Alberto; Bullock, Seth; Darabos, Christian; Tomassini, Marco; Giacobini, Mario

2017-11-01

The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.

Stable configurations in social networks

NASA Astrophysics Data System (ADS)

Bronski, Jared C.; DeVille, Lee; Ferguson, Timothy; Livesay, Michael

2018-06-01

We present and analyze a model of opinion formation on an arbitrary network whose dynamics comes from a global energy function. We study the global and local minimizers of this energy, which we call stable opinion configurations, and describe the global minimizers under certain assumptions on the friendship graph. We show a surprising result that the number of stable configurations is not necessarily monotone in the strength of connection in the social network, i.e. the model sometimes supports more stable configurations when the interpersonal connections are made stronger.

Topic-Aware Physical Activity Propagation in a Health Social Network

PubMed Central

Phan, Nhathai; Ebrahimi, Javid; Kil, Dave; Piniewski, Brigitte; Dou, Dejing

2016-01-01

Modeling physical activity propagation, such as physical exercise level and intensity, is the key to preventing the conduct that can lead to obesity; it can also help spread wellness behavior in a social network. PMID:27087794

Interests diffusion on a semantic multiplex. Comparing Computer Science and American Physical Society communities

NASA Astrophysics Data System (ADS)

D'Agostino, Gregorio; De Nicola, Antonio

2016-10-01

Exploiting the information about members of a Social Network (SN) represents one of the most attractive and dwelling subjects for both academic and applied scientists. The community of Complexity Science and especially those researchers working on multiplex social systems are devoting increasing efforts to outline general laws, models, and theories, to the purpose of predicting emergent phenomena in SN's (e.g. success of a product). On the other side the semantic web community aims at engineering a new generation of advanced services tailored to specific people needs. This implies defining constructs, models and methods for handling the semantic layer of SNs. We combined models and techniques from both the former fields to provide a hybrid approach to understand a basic (yet complex) phenomenon: the propagation of individual interests along the social networks. Since information may move along different social networks, one should take into account a multiplex structure. Therefore we introduced the notion of "Semantic Multiplex". In this paper we analyse two different semantic social networks represented by authors publishing in the Computer Science and those in the American Physical Society Journals. The comparison allows to outline common and specific features.

A user-centred methodology for designing an online social network to motivate health behaviour change.

PubMed

Kamal, Noreen; Fels, Sidney

2013-01-01

Positive health behaviour is critical to preventing illness and managing chronic conditions. A user-centred methodology was employed to design an online social network to motivate health behaviour change. The methodology was augmented by utilizing the Appeal, Belonging, Commitment (ABC) Framework, which is based on theoretical models for health behaviour change and use of online social networks. The user-centred methodology included four phases: 1) initial user inquiry on health behaviour and use of online social networks; 2) interview feedback on paper prototypes; 2) laboratory study on medium fidelity prototype; and 4) a field study on the high fidelity prototype. The points of inquiry through these phases were based on the ABC Framework. This yielded an online social network system that linked to external third party databases to deploy to users via an interactive website.

Emergence of hysteresis loop in social contagions on complex networks.

PubMed

Su, Zhen; Wang, Wei; Li, Lixiang; Xiao, Jinghua; Stanley, H Eugene

2017-07-21

Understanding the spreading mechanisms of social contagions in complex network systems has attracted much attention in the physics community. Here we propose a generalized threshold model to describe social contagions. Using extensive numerical simulations and theoretical analyses, we find that a hysteresis loop emerges in the system. Specifically, the steady state of the system is sensitive to the initial conditions of the dynamics of the system. In the steady state, the adoption size increases discontinuously with the transmission probability of information about social contagions, and trial size exhibits a non-monotonic pattern, i.e., it first increases discontinuously then decreases continuously. Finally we study social contagions on heterogeneous networks and find that network topology does not qualitatively affect our results.

Rumor spreading model with noise interference in complex social networks

NASA Astrophysics Data System (ADS)

Zhu, Liang; Wang, Youguo

2017-03-01

In this paper, a modified susceptible-infected-removed (SIR) model has been proposed to explore rumor diffusion on complex social networks. We take variation of connectivity into consideration and assume the variation as noise. On the basis of related literature on virus networks, the noise is described as standard Brownian motion while stochastic differential equations (SDE) have been derived to characterize dynamics of rumor diffusion both on homogeneous networks and heterogeneous networks. Then, theoretical analysis on homogeneous networks has been demonstrated to investigate the solution of SDE model and the steady state of rumor diffusion. Simulations both on Barabási-Albert (BA) network and Watts-Strogatz (WS) network display that the addition of noise accelerates rumor diffusion and expands diffusion size, meanwhile, the spreading speed on BA network is much faster than on WS network under the same noise intensity. In addition, there exists a rumor diffusion threshold in statistical average meaning on homogeneous network which is absent on heterogeneous network. Finally, we find a positive correlation between peak value of infected individuals and noise intensity while a negative correlation between rumor lifecycle and noise intensity overall.

The Reciprocal Relationship Between Social Connectedness and Mental Health Among Older European Adults: A SHARE-Based Analysis.

PubMed

Schwartz, Ella; Litwin, Howard

2017-11-04

The current study aimed to understand the reciprocal relationship between social networks and mental health in old age. It explored the dynamic aspects of that relationship and assessed the influence of social networks on mental health, as well as a concurrent influence of mental health on change in social connectedness. The data came from two measurement points in the Survey of Health, Aging and Retirement in Europe (SHARE). The analytic sample was composed of adults aged 65 years and above (N = 14,706). Analyses were conducted via latent change score models. Analyses showed a reciprocal association between social networks and mental health; baseline social connectedness led to mental health improvements and a better initial mental state led to richer social networks. The results further indicated that the relative effect of mental health on change in social network connectedness was greater than the corresponding effect of social network connectedness on change in mental health. No gender differences were found regarding the reciprocal associations. The results of this study demonstrate the dynamic inter-relationship of social networks and mental health. It highlights the need to take into account both directions of influence when studying the impact of social relationships on mental health. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Social Disadvantage and Network Turnover

PubMed Central

2015-01-01

Objectives. Research shows that socially disadvantaged groups—especially African Americans and people of low socioeconomic status (SES)—experience more unstable social environments. I argue that this causes higher rates of turnover within their personal social networks. This is a particularly important issue among disadvantaged older adults, who may benefit from stable networks. This article, therefore, examines whether social disadvantage is related to various aspects of personal network change. Method. Social network change was assessed using longitudinal egocentric network data from the National Social Life, Health, and Aging Project, a study of older adults conducted between 2005 and 2011. Data collection in Wave 2 included a technique for comparing respondents’ confidant network rosters between waves. Rates of network losses, deaths, and additions were modeled using multivariate Poisson regression. Results. African Americans and low-SES individuals lost more confidants—especially due to death—than did whites and college-educated respondents. African Americans also added more confidants than whites. However, neither African Americans nor low-SES individuals were able to match confidant losses with new additions to the extent that others did, resulting in higher levels of confidant network shrinkage. These trends are partly, but not entirely, explained by disadvantaged individuals’ poorer health and their greater risk of widowhood or marital dissolution. Discussion. Additional work is needed to shed light on the role played by race- and class-based segregation on group differences in social network turnover. Social gerontologists should examine the role these differences play in explaining the link between social disadvantage and important outcomes in later life, such as health decline. PMID:24997286

Social and spatial processes associated with childhood diarrheal disease in Matlab, Bangladesh.

PubMed

Perez-Heydrich, Carolina; Furgurson, Jill M; Giebultowicz, Sophia; Winston, Jennifer J; Yunus, Mohammad; Streatfield, Peter Kim; Emch, Michael

2013-01-01

We develop novel methods for conceptualizing geographic space and social networks to evaluate their respective and combined contributions to childhood diarrheal incidence. After defining maternal networks according to direct familial linkages between females, and road networks using satellite imagery of the study area, we use a spatial econometrics model to evaluate the significance of correlation terms relating childhood diarrheal incidence to the incidence observed within respective networks. Disease was significantly clustered within road networks across time, but only inconsistently correlated within maternal networks. These methods could be widely applied to systems in which both social and spatial processes jointly influence health outcomes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Diffusion on social networks: Survey data from rural villages in central China.

PubMed

Xiong, Hang; Wang, Puqing; Zhu, Yueji

2016-06-01

Empirical studies on social diffusions are often restricted by the access to data of diffusion and social relations on the same objects. We present a set of first-hand data that we collected in ten rural villages in central China through household surveys. The dataset contains detailed and comprehensive data of the diffusion of an innovation, the major social relationships and the household level demographic characteristics in these villages. The data have been used to study peer effects in social diffusion using simulation models, "Peer Effects and Social Network: The Case of Rural Diffusion in Central China" [1]. They can also be used to estimate spatial econometric models. Data are supplied with this article.

Selective Narrowing of Social Networks Across Adulthood is Associated With Improved Emotional Experience in Daily Life.

PubMed

English, Tammy; Carstensen, Laura L

2014-03-01

Past research has documented age differences in the size and composition of social networks that suggest that networks grow smaller with age and include an increasingly greater proportion of well-known social partners. According to socioemotional selectivity theory, such changes in social network composition serve an antecedent emotion regulatory function that supports an age-related increase in the priority that people place on emotional well-being. The present study employed a longitudinal design with a sample that spanned the full adult age range to examine whether there is evidence of within-individual (developmental) change in social networks and whether the characteristics of relationships predict emotional experiences in daily life. Using growth curve analyses, social networks were found to increase in size in young adulthood and then decline steadily throughout later life. As postulated by socioemotional selectivity theory, reductions were observed primarily in the number of peripheral partners; the number of close partners was relatively stable over time. In addition, cross-sectional analyses revealed that older adults reported that social network members elicited less negative emotion and more positive emotion. The emotional tone of social networks, particularly when negative emotions were associated with network members, also predicted experienced emotion of participants. Overall, findings were robust after taking into account demographic variables and physical health. The implications of these findings are discussed in the context of socioemotional selectivity theory and related theoretical models.

A social network perspective on heroin and cocaine use among adults: evidence of bidirectional influences.

PubMed

Bohnert, Amy S B; Bradshaw, Catherine P; Latkin, Carl A

2009-07-01

While several studies have documented a relationship between initiation of drug use and social network drug use in youth, the direction of this association is not well understood, particularly among adults or for stages of drug involvement beyond initiation. The present study sought to examine two competing theories (social selection and social influence) in the longitudinal relationship between drug use (heroin and/or cocaine) and social network drug use among drug-experienced adults. Three waves of data came from a cohort of 1108 adults reporting a life-time history of heroin and/or cocaine use. Low-income neighborhoods with high rates of drug use in Baltimore, Maryland. Participants had weekly contact with drug users and were 18 years of age or older. Drug use data were self-report. Network drug use was assessed through a social network inventory. Close friends were individuals whom the participant reported seeing daily or rated as having the highest level of trust. Findings Structural equation modeling indicated significant bidirectional influences. The majority of change in network drug use over time was due to change in the composition of the network rather than change in friends' behavior. Drug use by close peers did not influence participant drug use beyond the total network. There is evidence of both social selection and social influence processes in the association between drug use and network drug use among drug-experienced adults.

Network Characteristics, Perceived Social Support, and Psychological Adjustment in Mothers of Children with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Benson, Paul R.

2012-01-01

This study examined the characteristics of the support networks of 106 mothers of children with ASD and their relationship to perceived social support, depressed mood, and subjective well-being. Using structural equation modeling, two competing sets of hypotheses were assessed: (1) that network characteristics would impact psychological adjustment…

Having mentors and campus social networks moderates the impact of worries and video gaming on depressive symptoms: a moderated mediation analysis.

PubMed

Lee, Jong-Sun; Jeong, Bumseok

2014-05-05

Easy access to the internet has spawned a wealth of research to investigate the effects of its use on depression. However, one limitation of many previous studies is that they disregard the interactive mechanisms of risk and protective factors. The aim of the present study was to investigate a resilience model in the relationship between worry, daily internet video game playing, daily sleep duration, mentors, social networks and depression, using a moderated mediation analysis. 6068 Korean undergraduate and graduate students participated in this study. The participants completed a web-based mental health screening questionnaire including the Beck Depression Inventory (BDI) and information about number of worries, number of mentors, number of campus social networks, daily sleep duration, daily amount of internet video game playing and daily amount of internet searching on computer or smartphone. A moderated mediation analysis was carried out using the PROCESS macro which allowed the inclusion of mediators and moderator in the same model. The results showed that the daily amount of internet video game playing and daily sleep duration partially mediated the association between the number of worries and the severity of depression. In addition, the mediating effect of the daily amount of internet video game playing was moderated by both the number of mentors and the number of campus social networks. The current findings indicate that the negative impact of worry on depression through internet video game playing can be buffered when students seek to have a number of mentors and campus social networks. Interventions should therefore target individuals who have higher number of worries but seek only a few mentors or campus social networks. Social support via campus mentorship and social networks ameliorate the severity of depression in university students.

On investigating social dynamics in tactical opportunistic mobile networks

NASA Astrophysics Data System (ADS)

Gao, Wei; Li, Yong

2014-06-01

The efficiency of military mobile network operations at the tactical edge is challenging due to the practical Disconnected, Intermittent, and Limited (DIL) environments at the tactical edge which make it hard to maintain persistent end-to-end wireless network connectivity. Opportunistic mobile networks are hence devised to depict such tactical networking scenarios. Social relations among warfighters in tactical opportunistic mobile networks are implicitly represented by their opportunistic contacts via short-range radios, but were inappropriately considered as stationary over time by the conventional wisdom. In this paper, we develop analytical models to probabilistically investigate the temporal dynamics of this social relationship, which is critical to efficient mobile communication in the battlespace. We propose to formulate such dynamics by developing various sociological metrics, including centrality and community, with respect to the opportunistic mobile network contexts. These metrics investigate social dynamics based on the experimentally validated skewness of users' transient contact distributions over time.

Transformational leadership and group interaction as climate antecedents: a social network analysis.

PubMed

Zohar, Dov; Tenne-Gazit, Orly

2008-07-01

In order to test the social mechanisms through which organizational climate emerges, this article introduces a model that combines transformational leadership and social interaction as antecedents of climate strength (i.e., the degree of within-unit agreement about climate perceptions). Despite their longstanding status as primary variables, both antecedents have received limited empirical research. The sample consisted of 45 platoons of infantry soldiers from 5 different brigades, using safety climate as the exemplar. Results indicate a partially mediated model between transformational leadership and climate strength, with density of group communication network as the mediating variable. In addition, the results showed independent effects for group centralization of the communication and friendship networks, which exerted incremental effects on climate strength over transformational leadership. Whereas centralization of the communication network was found to be negatively related to climate strength, centralization of the friendship network was positively related to it. Theoretical and practical implications are discussed.

Dynamical origins of the community structure of an online multi-layer society

NASA Astrophysics Data System (ADS)

Klimek, Peter; Diakonova, Marina; Eguíluz, Víctor M.; San Miguel, Maxi; Thurner, Stefan

2016-08-01

Social structures emerge as a result of individuals managing a variety of different social relationships. Societies can be represented as highly structured dynamic multiplex networks. Here we study the dynamical origins of the specific community structures of a large-scale social multiplex network of a human society that interacts in a virtual world of a massive multiplayer online game. There we find substantial differences in the community structures of different social actions, represented by the various layers in the multiplex network. Community sizes distributions are either fat-tailed or appear to be centered around a size of 50 individuals. To understand these observations we propose a voter model that is built around the principle of triadic closure. It explicitly models the co-evolution of node- and link-dynamics across different layers of the multiplex network. Depending on link and node fluctuation probabilities, the model exhibits an anomalous shattered fragmentation transition, where one layer fragments from one large component into many small components. The observed community size distributions are in good agreement with the predicted fragmentation in the model. This suggests that several detailed features of the fragmentation in societies can be traced back to the triadic closure processes.

Selection Strategies for Social Influence in the Threshold Model

NASA Astrophysics Data System (ADS)

Karampourniotis, Panagiotis; Szymanski, Boleslaw; Korniss, Gyorgy

The ubiquity of online social networks makes the study of social influence extremely significant for its applications to marketing, politics and security. Maximizing the spread of influence by strategically selecting nodes as initiators of a new opinion or trend is a challenging problem. We study the performance of various strategies for selection of large fractions of initiators on a classical social influence model, the Threshold model (TM). Under the TM, a node adopts a new opinion only when the fraction of its first neighbors possessing that opinion exceeds a pre-assigned threshold. The strategies we study are of two kinds: strategies based solely on the initial network structure (Degree-rank, Dominating Sets, PageRank etc.) and strategies that take into account the change of the states of the nodes during the evolution of the cascade, e.g. the greedy algorithm. We find that the performance of these strategies depends largely on both the network structure properties, e.g. the assortativity, and the distribution of the thresholds assigned to the nodes. We conclude that the optimal strategy needs to combine the network specifics and the model specific parameters to identify the most influential spreaders. Supported in part by ARL NS-CTA, ARO, and ONR.

Disease dynamics in a dynamic social network

NASA Astrophysics Data System (ADS)

Christensen, Claire; Albert, István; Grenfell, Bryan; Albert, Réka

2010-07-01

We develop a framework for simulating a realistic, evolving social network (a city) into which a disease is introduced. We compare our results to prevaccine era measles data for England and Wales, and find that they capture the quantitative and qualitative features of epidemics in populations spanning two orders of magnitude. Our results provide unique insight into how and why the social topology of the contact network influences the propagation of the disease through the population. We argue that network simulation is suitable for concurrently probing contact network dynamics and disease dynamics in ways that prior modeling approaches cannot and it can be extended to the study of less well-documented diseases.

Visualising the invisible: a network approach to reveal the informal social side of student learning.

PubMed

Hommes, J; Rienties, B; de Grave, W; Bos, G; Schuwirth, L; Scherpbier, A

2012-12-01

World-wide, universities in health sciences have transformed their curriculum to include collaborative learning and facilitate the students' learning process. Interaction has been acknowledged to be the synergistic element in this learning context. However, students spend the majority of their time outside their classroom and interaction does not stop outside the classroom. Therefore we studied how informal social interaction influences student learning. Moreover, to explore what really matters in the students learning process, a model was tested how the generally known important constructs-prior performance, motivation and social integration-relate to informal social interaction and student learning. 301 undergraduate medical students participated in this cross-sectional quantitative study. Informal social interaction was assessed using self-reported surveys following the network approach. Students' individual motivation, social integration and prior performance were assessed by the Academic Motivation Scale, the College Adaption Questionnaire and students' GPA respectively. A factual knowledge test represented student' learning. All social networks were positively associated with student learning significantly: friendships (β = 0.11), providing information to other students (β = 0.16), receiving information from other students (β = 0.25). Structural equation modelling revealed a model in which social networks increased student learning (r = 0.43), followed by prior performance (r = 0.31). In contrast to prior literature, students' academic motivation and social integration were not associated with students' learning. Students' informal social interaction is strongly associated with students' learning. These findings underline the need to change our focus from the formal context (classroom) to the informal context to optimize student learning and deliver modern medics.

Epidemic spreading in networks with nonrandom long-range interactions

NASA Astrophysics Data System (ADS)

Estrada, Ernesto; Kalala-Mutombo, Franck; Valverde-Colmeiro, Alba

2011-09-01

An “infection,” understood here in a very broad sense, can be propagated through the network of social contacts among individuals. These social contacts include both “close” contacts and “casual” encounters among individuals in transport, leisure, shopping, etc. Knowing the first through the study of the social networks is not a difficult task, but having a clear picture of the network of casual contacts is a very hard problem in a society of increasing mobility. Here we assume, on the basis of several pieces of empirical evidence, that the casual contacts between two individuals are a function of their social distance in the network of close contacts. Then, we assume that we know the network of close contacts and infer the casual encounters by means of nonrandom long-range (LR) interactions determined by the social proximity of the two individuals. This approach is then implemented in a susceptible-infected-susceptible (SIS) model accounting for the spread of infections in complex networks. A parameter called “conductance” controls the feasibility of those casual encounters. In a zero conductance network only contagion through close contacts is allowed. As the conductance increases the probability of having casual encounters also increases. We show here that as the conductance parameter increases, the rate of propagation increases dramatically and the infection is less likely to die out. This increment is particularly marked in networks with scale-free degree distributions, where infections easily become epidemics. Our model provides a general framework for studying epidemic spreading in networks with arbitrary topology with and without casual contacts accounted for by means of LR interactions.

Epidemic spreading in networks with nonrandom long-range interactions.

PubMed

Estrada, Ernesto; Kalala-Mutombo, Franck; Valverde-Colmeiro, Alba

2011-09-01

An "infection," understood here in a very broad sense, can be propagated through the network of social contacts among individuals. These social contacts include both "close" contacts and "casual" encounters among individuals in transport, leisure, shopping, etc. Knowing the first through the study of the social networks is not a difficult task, but having a clear picture of the network of casual contacts is a very hard problem in a society of increasing mobility. Here we assume, on the basis of several pieces of empirical evidence, that the casual contacts between two individuals are a function of their social distance in the network of close contacts. Then, we assume that we know the network of close contacts and infer the casual encounters by means of nonrandom long-range (LR) interactions determined by the social proximity of the two individuals. This approach is then implemented in a susceptible-infected-susceptible (SIS) model accounting for the spread of infections in complex networks. A parameter called "conductance" controls the feasibility of those casual encounters. In a zero conductance network only contagion through close contacts is allowed. As the conductance increases the probability of having casual encounters also increases. We show here that as the conductance parameter increases, the rate of propagation increases dramatically and the infection is less likely to die out. This increment is particularly marked in networks with scale-free degree distributions, where infections easily become epidemics. Our model provides a general framework for studying epidemic spreading in networks with arbitrary topology with and without casual contacts accounted for by means of LR interactions.

Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks

PubMed Central

Portela, Javier; García Villalba, Luis Javier; Silva Trujillo, Alejandra Guadalupe; Sandoval Orozco, Ana Lucila; Kim, Tai-Hoon

2016-01-01

Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users’ network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders’ or receivers’ identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks. PMID:27809275

Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks.

PubMed

Portela, Javier; García Villalba, Luis Javier; Silva Trujillo, Alejandra Guadalupe; Sandoval Orozco, Ana Lucila; Kim, Tai-Hoon

2016-11-01

Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users' network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders' or receivers' identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks.

Bidirectional selection between two classes in complex social networks.

PubMed

Zhou, Bin; He, Zhe; Jiang, Luo-Luo; Wang, Nian-Xin; Wang, Bing-Hong

2014-12-19

The bidirectional selection between two classes widely emerges in various social lives, such as commercial trading and mate choosing. Until now, the discussions on bidirectional selection in structured human society are quite limited. We demonstrated theoretically that the rate of successfully matching is affected greatly by individuals' neighborhoods in social networks, regardless of the type of networks. Furthermore, it is found that the high average degree of networks contributes to increasing rates of successful matches. The matching performance in different types of networks has been quantitatively investigated, revealing that the small-world networks reinforces the matching rate more than scale-free networks at given average degree. In addition, our analysis is consistent with the modeling result, which provides the theoretical understanding of underlying mechanisms of matching in complex networks.

Complex contagions with timers

NASA Astrophysics Data System (ADS)

Oh, Se-Wook; Porter, Mason A.

2018-03-01

There has been a great deal of effort to try to model social influence—including the spread of behavior, norms, and ideas—on networks. Most models of social influence tend to assume that individuals react to changes in the states of their neighbors without any time delay, but this is often not true in social contexts, where (for various reasons) different agents can have different response times. To examine such situations, we introduce the idea of a timer into threshold models of social influence. The presence of timers on nodes delays adoptions—i.e., changes of state—by the agents, which in turn delays the adoptions of their neighbors. With a homogeneously-distributed timer, in which all nodes have the same amount of delay, the adoption order of nodes remains the same. However, heterogeneously-distributed timers can change the adoption order of nodes and hence the "adoption paths" through which state changes spread in a network. Using a threshold model of social contagions, we illustrate that heterogeneous timers can either accelerate or decelerate the spread of adoptions compared to an analogous situation with homogeneous timers, and we investigate the relationship of such acceleration or deceleration with respect to the timer distribution and network structure. We derive an analytical approximation for the temporal evolution of the fraction of adopters by modifying a pair approximation for the Watts threshold model, and we find good agreement with numerical simulations. We also examine our new timer model on networks constructed from empirical data.

Variability in personality expression across contexts: a social network approach.

PubMed

Clifton, Allan

2014-04-01

The current research investigated how the contextual expression of personality differs across interpersonal relationships. Two related studies were conducted with college samples (Study 1: N = 52, 38 female; Study 2: N = 111, 72 female). Participants in each study completed a five-factor measure of personality and constructed a social network detailing their 30 most important relationships. Participants used a brief Five-Factor Model scale to rate their personality as they experience it when with each person in their social network. Multiple informants selected from each social network then rated the target participant's personality (Study 1: N = 227, Study 2: N = 777). Contextual personality ratings demonstrated incremental validity beyond standard global self-report in predicting specific informants' perceptions. Variability in these contextualized personality ratings was predicted by the position of the other individuals within the social network. Across both studies, participants reported being more extraverted and neurotic, and less conscientious, with more central members of their social networks. Dyadic social network-based assessments of personality provide incremental validity in understanding personality, revealing dynamic patterns of personality variability unobservable with standard assessment techniques. © 2013 Wiley Periodicals, Inc.

Common neighbour structure and similarity intensity in complex networks

NASA Astrophysics Data System (ADS)

Hou, Lei; Liu, Kecheng

2017-10-01

Complex systems as networks always exhibit strong regularities, implying underlying mechanisms governing their evolution. In addition to the degree preference, the similarity has been argued to be another driver for networks. Assuming a network is randomly organised without similarity preference, the present paper studies the expected number of common neighbours between vertices. A symmetrical similarity index is accordingly developed by removing such expected number from the observed common neighbours. The developed index can not only describe the similarities between vertices, but also the dissimilarities. We further apply the proposed index to measure of the influence of similarity on the wring patterns of networks. Fifteen empirical networks as well as artificial networks are examined in terms of similarity intensity and degree heterogeneity. Results on real networks indicate that, social networks are strongly governed by the similarity as well as the degree preference, while the biological networks and infrastructure networks show no apparent similarity governance. Particularly, classical network models, such as the Barabási-Albert model, the Erdös-Rényi model and the Ring Lattice, cannot well describe the social networks in terms of the degree heterogeneity and similarity intensity. The findings may shed some light on the modelling and link prediction of different classes of networks.

Development and Analyses of Privacy Management Models in Online Social Networks Based on Communication Privacy Management Theory

ERIC Educational Resources Information Center

Lee, Ki Jung

2013-01-01

Online social networks (OSNs), while serving as an emerging means of communication, promote various issues of privacy. Users of OSNs encounter diverse occasions that lead to invasion of their privacy, e.g., published conversation, public revelation of their personally identifiable information, and open boundary of distinct social groups within…

The Correlation of Students' Classroom-Assigned Time Social Networking with TAKS Literacy Scores

ERIC Educational Resources Information Center

Bicknell, Angela

2012-01-01

Education has continued to follow a traditional teaching model which may not prepare students with needed workforce skills. Social networking has been viewed as a technology tool useful for enhancing communication at both the business and educational level. The theory of connectivism underscores the need for social group interaction to provide…

Delinquency, Social Skills and the Structure of Peer Relations: Assessing Criminological Theories by Social Network Theory

ERIC Educational Resources Information Center

Smangs, Mattias

2010-01-01

This article explores the plausibility of the conflicting theoretical assumptions underlying the main criminological perspectives on juvenile delinquents, their peer relations and social skills: the social ability model, represented by Sutherland's theory of differential associations, and the social disability model, represented by Hirschi's…

ANN multiscale model of anti-HIV drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks.

PubMed

González-Díaz, Humberto; Herrera-Ibatá, Diana María; Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Orbegozo-Medina, Ricardo Alfredo; Pazos, Alejandro

2014-03-24

This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures.

Perceptions of Suicide Stigma.

PubMed

Frey, Laura M; Hans, Jason D; Cerel, Julie

2016-03-01

Previous research has failed to examine perceptions of stigma experienced by individuals with a history of suicidal behavior, and few studies have examined how stigma is experienced based on whether it was perceived from treatment providers or social network members. This study examined stigma experienced by individuals with previous suicidal behavior from both treatment providers and individuals in one's social and family networks. Individuals (n = 156) with a lifetime history of suicidal behavior were recruited through the American Association of Suicidology listserv. Respondents reported the highest rates of perceived stigma with a close family member (57.1%) and emergency department personnel (56.6%). Results indicated that individuals with previous suicidal behavior were more likely to experience stigma from non-mental health providers and social network members than from mental health providers. A hierarchical regression model including both source and type of stigma accounted for more variance (R(2) = .14) in depression symptomology than a model (R(2) = .06) with only type of stigma. Prevalence of stigma perceived from social network members was the best predictor of depression symptom severity. These findings highlight the need for future research on how social network members react to suicide disclosure and potential interventions for improving interactions following disclosure.

The Role of Religiousness/Spirituality and Social Networks in Predicting Depressive Symptoms among Older Korean Americans.

PubMed

Lee, Yeon-Shim; Park, So-Young; Roh, Soonhee; Koenig, Harold G; Yoo, Grace J

2017-06-01

This study (1) examined the effects of religiousness/spirituality and social networks as predictors of depressive symptoms in older Korean Americans and (2) compared the best predictors of depressive symptoms. A cross-sectional survey was conducted with 200 older Korean Americans residing in the New York City area in 2009. Best-subsets regression analyses were used to evaluate the best predictors of depressive symptoms. Nearly 30% of older Korean participants reported mild or severe depressive symptoms. The best model fit for depressive symptoms involved four predictors: physical health status, religious/spiritual coping skills, social networks, and annual household income. Social networks and religious/spiritual coping skills contributed significantly to the variance of depressive symptoms. Adding additional variables to the model did not enhance predictive and descriptive power. Religiousness/spirituality and social networks are important for coping with life stress and may be useful in developing effective health care strategies in the management of depression among older Korean Americans. Health education and intervention could be framed in ways that strengthen such coping resources for this population. Future research is needed to best guide prevention and intervention strategies.

A stochastic agent-based model of pathogen propagation in dynamic multi-relational social networks

PubMed Central

Khan, Bilal; Dombrowski, Kirk; Saad, Mohamed

2015-01-01

We describe a general framework for modeling and stochastic simulation of epidemics in realistic dynamic social networks, which incorporates heterogeneity in the types of individuals, types of interconnecting risk-bearing relationships, and types of pathogens transmitted across them. Dynamism is supported through arrival and departure processes, continuous restructuring of risk relationships, and changes to pathogen infectiousness, as mandated by natural history; dynamism is regulated through constraints on the local agency of individual nodes and their risk behaviors, while simulation trajectories are validated using system-wide metrics. To illustrate its utility, we present a case study that applies the proposed framework towards a simulation of HIV in artificial networks of intravenous drug users (IDUs) modeled using data collected in the Social Factors for HIV Risk survey. PMID:25859056

Towards a portal and search engine to facilitate academic and research collaboration in engineering and education

NASA Astrophysics Data System (ADS)

Bonilla Villarreal, Isaura Nathaly

While international academic and research collaborations are of great importance at this time, it is not easy to find researchers in the engineering field that publish in languages other than English. Because of this disconnect, there exists a need for a portal to find Who's Who in Engineering Education in the Americas. The objective of this thesis is to built an object-oriented architecture for this proposed portal. The Unified Modeling Language (UML) model developed in this thesis incorporates the basic structure of a social network for academic purposes. Reverse engineering of three social networks portals yielded important aspects of their structures that have been incorporated in the proposed UML model. Furthermore, the present work includes a pattern for academic social networks..

Evolution of the social network of scientific collaborations

NASA Astrophysics Data System (ADS)

Barabási, A. L.; Jeong, H.; Néda, Z.; Ravasz, E.; Schubert, A.; Vicsek, T.

2002-08-01

The co-authorship network of scientists represents a prototype of complex evolving networks. In addition, it offers one of the most extensive database to date on social networks. By mapping the electronic database containing all relevant journals in mathematics and neuro-science for an 8-year period (1991-98), we infer the dynamic and the structural mechanisms that govern the evolution and topology of this complex system. Three complementary approaches allow us to obtain a detailed characterization. First, empirical measurements allow us to uncover the topological measures that characterize the network at a given moment, as well as the time evolution of these quantities. The results indicate that the network is scale-free, and that the network evolution is governed by preferential attachment, affecting both internal and external links. However, in contrast with most model predictions the average degree increases in time, and the node separation decreases. Second, we propose a simple model that captures the network's time evolution. In some limits the model can be solved analytically, predicting a two-regime scaling in agreement with the measurements. Third, numerical simulations are used to uncover the behavior of quantities that could not be predicted analytically. The combined numerical and analytical results underline the important role internal links play in determining the observed scaling behavior and network topology. The results and methodologies developed in the context of the co-authorship network could be useful for a systematic study of other complex evolving networks as well, such as the world wide web, Internet, or other social networks.

Social networks of HIV-positive women and their association with social support and depression symptoms.

PubMed

Cederbaum, Julie A; Rice, Eric; Craddock, Jaih; Pimentel, Veronica; Beaver, Patty

2017-02-01

Social support is important to the mental health and well-being of HIV-positive women. Limited information exists about the specific structure and composition of HIV-positive women's support networks or associations of these network properties with mental health outcomes. In this pilot study, the authors examine whether support network characteristics were associated with depressive symptoms. Survey and network data were collected from HIV-positive women (N = 46) via a web-based survey and an iPad application in August 2012. Data were analyzed using multivariate linear regression models in SAS. Depressive symptoms were positively associated with a greater number of doctors in a woman's network; having more HIV-positive network members was associated with less symptom reporting. Women who reported more individuals who could care for them had more family support. Those who reported feeling loved were less likely to report disclosure stigma. This work highlighted that detailed social network data can increase our understanding of social support so as to identify interventions to support the mental health of HIV-positive women. Most significant is the ongoing need for support from peers.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure

PubMed Central

Schleussner, Carl-Friedrich; Donges, Jonathan F.; Engemann, Denis A.; Levermann, Anders

2016-01-01

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking. PMID:27510641

An opinion-driven behavioral dynamics model for addictive behaviors

DOE PAGES

Moore, Thomas W.; Finley, Patrick D.; Apelberg, Benjamin J.; ...

2015-04-08

We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual’s behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Additionally, individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters providemore » targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. Furthermore, this has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.« less

Effective spreading from multiple leaders identified by percolation in the susceptible-infected-recovered (SIR) model

NASA Astrophysics Data System (ADS)

Ji, Shenggong; Lü, Linyuan; Yeung, Chi Ho; Hu, Yanqing

2017-07-01

Social networks constitute a new platform for information propagation, but its success is crucially dependent on the choice of spreaders who initiate the spreading of information. In this paper, we remove edges in a network at random and the network segments into isolated clusters. The most important nodes in each cluster then form a set of influential spreaders, such that news propagating from them would lead to extensive coverage and minimal redundancy. The method utilizes the similarities between the segmented networks before percolation and the coverage of information propagation in each social cluster to obtain a set of distributed and coordinated spreaders. Our tests of implementing the susceptible-infected-recovered model on Facebook and Enron email networks show that this method outperforms conventional centrality-based methods in terms of spreadability and coverage redundancy. The suggested way of identifying influential spreaders thus sheds light on a new paradigm of information propagation in social networks.

Modeling information diffusion in time-varying community networks

NASA Astrophysics Data System (ADS)

Cui, Xuelian; Zhao, Narisa

2017-12-01

Social networks are rarely static, and they typically have time-varying network topologies. A great number of studies have modeled temporal networks and explored social contagion processes within these models; however, few of these studies have considered community structure variations. In this paper, we present a study of how the time-varying property of a modular structure influences the information dissemination. First, we propose a continuous-time Markov model of information diffusion where two parameters, mobility rate and community attractiveness, are introduced to address the time-varying nature of the community structure. The basic reproduction number is derived, and the accuracy of this model is evaluated by comparing the simulation and theoretical results. Furthermore, numerical results illustrate that generally both the mobility rate and community attractiveness significantly promote the information diffusion process, especially in the initial outbreak stage. Moreover, the strength of this promotion effect is much stronger when the modularity is higher. Counterintuitively, it is found that when all communities have the same attractiveness, social mobility no longer accelerates the diffusion process. In addition, we show that the local spreading in the advantage group has been greatly enhanced due to the agglomeration effect caused by the social mobility and community attractiveness difference, which thus increases the global spreading.

Collective iteration behavior for online social networks

NASA Astrophysics Data System (ADS)

Liu, Jian-Guo; Li, Ren-De; Guo, Qiang; Zhang, Yi-Cheng

2018-06-01

Understanding the patterns of collective behavior in online social network (OSNs) is critical to expanding the knowledge of human behavior and tie relationship. In this paper, we investigate a specific pattern called social signature in Facebook and Wiki users' online communication behaviors, capturing the distribution of frequency of interactions between different alters over time in the ego network. The empirical results show that there are robust social signatures of interactions no matter how friends change over time, which indicates that a stable commutation pattern exists in online communication. By comparing a random null model, we find the that commutation pattern is heterogeneous between ego and alters. Furthermore, in order to regenerate the pattern of the social signature, we present a preferential interaction model, which assumes that new users intend to look for the old users with strong ties while old users have tendency to interact with new friends. The experimental results show that the presented model can reproduce the heterogeneity of social signature by adjusting 2 parameters, the number of communicating targets m and the max number of interactions n, for Facebook users, m = n = 5, for Wiki users, m = 2 and n = 8. This work helps in deeply understanding the regularity of social signature.

The Peer Social Networks of Young Children with Down Syndrome in Classroom Programmes

PubMed Central

Guralnick, Michael J.; Connor, Robert T.; Johnson, L. Clark

2010-01-01

Background The nature and characteristics of the peer social networks of young children with Down syndrome in classroom settings were examined within a developmental framework. Method Comparisons were made with younger typically developing children matched on mental age and typically developing children matched on chronological age. Results Similar patterns were found for all three groups for most peer social network measures. However, group differences were obtained for measures of teacher assistance and peer interactions in unstructured situations. Conclusions Positive patterns appeared to be related to the social orientation of children with Down syndrome and the special efforts of teachers to support children’s peer social networks. Findings also suggested that fundamental peer competence problems for children with Down syndrome remain and may best be addressed within the framework of contemporary models of peer-related social competence. PMID:21765644

Fundamental structures of dynamic social networks.

PubMed

Sekara, Vedran; Stopczynski, Arkadiusz; Lehmann, Sune

2016-09-06

Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision.

Fundamental structures of dynamic social networks

PubMed Central

Sekara, Vedran; Stopczynski, Arkadiusz; Lehmann, Sune

2016-01-01

Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision. PMID:27555584

Evidence of social network influence on multiple HIV risk behaviors and normative beliefs among young Tanzanian men

PubMed Central

Mulawa, Marta; Yamanis, Thespina J.; Hill, Lauren; Balvanz, Peter; Kajula, Lusajo J.; Maman, Suzanne

2016-01-01

Research on network-level influences on HIV risk behaviors among young men in sub-Saharan Africa is severely lacking. One significant gap in the literature that may provide direction for future research with this population is understanding the degree to which various HIV risk behaviors and normative beliefs cluster within men’s social networks. Such research may help us understand which HIV-related norms and behaviors have the greatest potential to be changed through social influence. Additionally, few network-based studies have described the structure of social networks of young men in sub-Saharan Africa. Understanding the structure of men’s peer networks may motivate future research examining the ways in which network structures shape the spread of information, adoption of norms, and diffusion of behaviors. We contribute to filling these gaps by using social network analysis and multilevel modeling to describe a unique dataset of mostly young men (n= 1,249 men and 242 women) nested within 59 urban social networks in Dar es Salaam, Tanzania. We examine the means, ranges, and clustering of men’s HIV-related normative beliefs and behaviors. Networks in this urban setting varied substantially in both composition and structure and a large proportion of men engaged in risky behaviors including inconsistent condom use, sexual partner concurrency, and intimate partner violence perpetration. We found significant clustering of normative beliefs and risk behaviors within these men’s social networks. Specifically, network membership explained between 5.78 and 7.17% of variance in men’s normative beliefs and between 1.93 and 15.79% of variance in risk behaviors. Our results suggest that social networks are important socialization sites for young men and may influence the adoption of norms and behaviors. We conclude by calling for more research on men’s social networks in Sub-Saharan Africa and map out several areas of future inquiry. PMID:26874081

Evidence of social network influence on multiple HIV risk behaviors and normative beliefs among young Tanzanian men.

PubMed

Mulawa, Marta; Yamanis, Thespina J; Hill, Lauren M; Balvanz, Peter; Kajula, Lusajo J; Maman, Suzanne

2016-03-01

Research on network-level influences on HIV risk behaviors among young men in sub-Saharan Africa is severely lacking. One significant gap in the literature that may provide direction for future research with this population is understanding the degree to which various HIV risk behaviors and normative beliefs cluster within men's social networks. Such research may help us understand which HIV-related norms and behaviors have the greatest potential to be changed through social influence. Additionally, few network-based studies have described the structure of social networks of young men in sub-Saharan Africa. Understanding the structure of men's peer networks may motivate future research examining the ways in which network structures shape the spread of information, adoption of norms, and diffusion of behaviors. We contribute to filling these gaps by using social network analysis and multilevel modeling to describe a unique dataset of mostly young men (n = 1249 men and 242 women) nested within 59 urban social networks in Dar es Salaam, Tanzania. We examine the means, ranges, and clustering of men's HIV-related normative beliefs and behaviors. Networks in this urban setting varied substantially in both composition and structure and a large proportion of men engaged in risky behaviors including inconsistent condom use, sexual partner concurrency, and intimate partner violence perpetration. We found significant clustering of normative beliefs and risk behaviors within these men's social networks. Specifically, network membership explained between 5.78 and 7.17% of variance in men's normative beliefs and between 1.93 and 15.79% of variance in risk behaviors. Our results suggest that social networks are important socialization sites for young men and may influence the adoption of norms and behaviors. We conclude by calling for more research on men's social networks in Sub-Saharan Africa and map out several areas of future inquiry. Copyright © 2016 Elsevier Ltd. All rights reserved.

Coupling effect of nodes popularity and similarity on social network persistence

PubMed Central

Jin, Xiaogang; Jin, Cheng; Huang, Jiaxuan; Min, Yong

2017-01-01

Network robustness represents the ability of networks to withstand failures and perturbations. In social networks, maintenance of individual activities, also called persistence, is significant towards understanding robustness. Previous works usually consider persistence on pre-generated network structures; while in social networks, the network structure is growing with the cascading inactivity of existed individuals. Here, we address this challenge through analysis for nodes under a coevolution model, which characterizes individual activity changes under three network growth modes: following the descending order of nodes’ popularity, similarity or uniform random. We show that when nodes possess high spontaneous activities, a popularity-first growth mode obtains highly persistent networks; otherwise, with low spontaneous activities, a similarity-first mode does better. Moreover, a compound growth mode, with the consecutive joining of similar nodes in a short period and mixing a few high popularity nodes, obtains the highest persistence. Therefore, nodes similarity is essential for persistent social networks, while properly coupling popularity with similarity further optimizes the persistence. This demonstrates the evolution of nodes activity not only depends on network topology, but also their connective typology. PMID:28220840

Coupling effect of nodes popularity and similarity on social network persistence

NASA Astrophysics Data System (ADS)

Jin, Xiaogang; Jin, Cheng; Huang, Jiaxuan; Min, Yong

2017-02-01

Network robustness represents the ability of networks to withstand failures and perturbations. In social networks, maintenance of individual activities, also called persistence, is significant towards understanding robustness. Previous works usually consider persistence on pre-generated network structures; while in social networks, the network structure is growing with the cascading inactivity of existed individuals. Here, we address this challenge through analysis for nodes under a coevolution model, which characterizes individual activity changes under three network growth modes: following the descending order of nodes’ popularity, similarity or uniform random. We show that when nodes possess high spontaneous activities, a popularity-first growth mode obtains highly persistent networks; otherwise, with low spontaneous activities, a similarity-first mode does better. Moreover, a compound growth mode, with the consecutive joining of similar nodes in a short period and mixing a few high popularity nodes, obtains the highest persistence. Therefore, nodes similarity is essential for persistent social networks, while properly coupling popularity with similarity further optimizes the persistence. This demonstrates the evolution of nodes activity not only depends on network topology, but also their connective typology.

Coupling effect of nodes popularity and similarity on social network persistence.

PubMed

Jin, Xiaogang; Jin, Cheng; Huang, Jiaxuan; Min, Yong

2017-02-21

Network robustness represents the ability of networks to withstand failures and perturbations. In social networks, maintenance of individual activities, also called persistence, is significant towards understanding robustness. Previous works usually consider persistence on pre-generated network structures; while in social networks, the network structure is growing with the cascading inactivity of existed individuals. Here, we address this challenge through analysis for nodes under a coevolution model, which characterizes individual activity changes under three network growth modes: following the descending order of nodes' popularity, similarity or uniform random. We show that when nodes possess high spontaneous activities, a popularity-first growth mode obtains highly persistent networks; otherwise, with low spontaneous activities, a similarity-first mode does better. Moreover, a compound growth mode, with the consecutive joining of similar nodes in a short period and mixing a few high popularity nodes, obtains the highest persistence. Therefore, nodes similarity is essential for persistent social networks, while properly coupling popularity with similarity further optimizes the persistence. This demonstrates the evolution of nodes activity not only depends on network topology, but also their connective typology.

Coupling effects on turning points of infectious diseases epidemics in scale-free networks.

PubMed

Kim, Kiseong; Lee, Sangyeon; Lee, Doheon; Lee, Kwang Hyung

2017-05-31

Pandemic is a typical spreading phenomenon that can be observed in the human society and is dependent on the structure of the social network. The Susceptible-Infective-Recovered (SIR) model describes spreading phenomena using two spreading factors; contagiousness (β) and recovery rate (γ). Some network models are trying to reflect the social network, but the real structure is difficult to uncover. We have developed a spreading phenomenon simulator that can input the epidemic parameters and network parameters and performed the experiment of disease propagation. The simulation result was analyzed to construct a new marker VRTP distribution. We also induced the VRTP formula for three of the network mathematical models. We suggest new marker VRTP (value of recovered on turning point) to describe the coupling between the SIR spreading and the Scale-free (SF) network and observe the aspects of the coupling effects with the various of spreading and network parameters. We also derive the analytic formulation of VRTP in the fully mixed model, the configuration model, and the degree-based model respectively in the mathematical function form for the insights on the relationship between experimental simulation and theoretical consideration. We discover the coupling effect between SIR spreading and SF network through devising novel marker VRTP which reflects the shifting effect and relates to entropy.

Engineering Online and In-person Social Networks for Physical Activity: A Randomized Trial

PubMed Central

Rovniak, Liza S.; Kong, Lan; Hovell, Melbourne F.; Ding, Ding; Sallis, James F.; Ray, Chester A.; Kraschnewski, Jennifer L.; Matthews, Stephen A.; Kiser, Elizabeth; Chinchilli, Vernon M.; George, Daniel R.; Sciamanna, Christopher N.

2016-01-01

Background Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. Purpose To conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively-measured outcomes. Methods Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3% male, 83.4% overweight/obese) were randomized to 1 of 3 groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking, and prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Results Participants increased their MVPA by 21.0 mins/week, 95% CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Conclusions Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. Trial Registration Number NCT01142804 PMID:27405724

Associations of a social network typology with physical and mental health risks among older adults in South Korea.

PubMed

Park, N S; Jang, Y; Lee, B S; Chiriboga, D A; Chang, S; Kim, S Y

2018-05-01

The objectives of this study were to (1) develop an empirical typology of social networks in older Koreans; and (2) examine its effect on physical and mental health. A sample of 6900 community-dwelling older adults in South Korea was drawn from the 2014 Korean National Elderly Survey. Latent profile analysis (LPA) was conducted to derive social network types using eight common social network characteristics (marital status, living arrangement, the number and frequency of contact with close family/relatives, the number and frequency of contact with close friends, frequency of participation in social activities, and frequency of having visitors at home). The identified typologies were then regressed on self-rated health and depressive symptoms to explore the health risks posed by the group membership. The LPA identified a model with five types of social network as being most optimal (BIC = 153,848.34, entropy = .90). The groups were named diverse/family (enriched networks with more engagement with family), diverse/friend (enriched networks with more engagement with friends), friend-focused (high engagement with friends), distant (structurally disengaged), and restricted (structurally engaged but disengaged in family/friends networks). A series of regression analyses showed that membership in the restricted type was associated with more health and mental health risks than all types of social networks except the distant type. Findings demonstrate the importance of family and friends as a source of social network and call attention to not only structural but also non-structural aspects of social isolation. Findings and implications are discussed in cultural contexts.

Evolution of opinions on social networks in the presence of competing committed groups.

PubMed

Xie, Jierui; Emenheiser, Jeffrey; Kirby, Matthew; Sreenivasan, Sameet; Szymanski, Boleslaw K; Korniss, Gyorgy

2012-01-01

Public opinion is often affected by the presence of committed groups of individuals dedicated to competing points of view. Using a model of pairwise social influence, we study how the presence of such groups within social networks affects the outcome and the speed of evolution of the overall opinion on the network. Earlier work indicated that a single committed group within a dense social network can cause the entire network to quickly adopt the group's opinion (in times scaling logarithmically with the network size), so long as the committed group constitutes more than about 10% of the population (with the findings being qualitatively similar for sparse networks as well). Here we study the more general case of opinion evolution when two groups committed to distinct, competing opinions A and B, and constituting fractions pA and pB of the total population respectively, are present in the network. We show for stylized social networks (including Erdös-Rényi random graphs and Barabási-Albert scale-free networks) that the phase diagram of this system in parameter space (pA,pB) consists of two regions, one where two stable steady-states coexist, and the remaining where only a single stable steady-state exists. These two regions are separated by two fold-bifurcation (spinodal) lines which meet tangentially and terminate at a cusp (critical point). We provide further insights to the phase diagram and to the nature of the underlying phase transitions by investigating the model on infinite (mean-field limit), finite complete graphs and finite sparse networks. For the latter case, we also derive the scaling exponent associated with the exponential growth of switching times as a function of the distance from the critical point.

Evolution of Opinions on Social Networks in the Presence of Competing Committed Groups

PubMed Central

Xie, Jierui; Emenheiser, Jeffrey; Kirby, Matthew; Sreenivasan, Sameet; Szymanski, Boleslaw K.; Korniss, Gyorgy

2012-01-01

Public opinion is often affected by the presence of committed groups of individuals dedicated to competing points of view. Using a model of pairwise social influence, we study how the presence of such groups within social networks affects the outcome and the speed of evolution of the overall opinion on the network. Earlier work indicated that a single committed group within a dense social network can cause the entire network to quickly adopt the group's opinion (in times scaling logarithmically with the network size), so long as the committed group constitutes more than about of the population (with the findings being qualitatively similar for sparse networks as well). Here we study the more general case of opinion evolution when two groups committed to distinct, competing opinions and , and constituting fractions and of the total population respectively, are present in the network. We show for stylized social networks (including Erdös-Rényi random graphs and Barabási-Albert scale-free networks) that the phase diagram of this system in parameter space consists of two regions, one where two stable steady-states coexist, and the remaining where only a single stable steady-state exists. These two regions are separated by two fold-bifurcation (spinodal) lines which meet tangentially and terminate at a cusp (critical point). We provide further insights to the phase diagram and to the nature of the underlying phase transitions by investigating the model on infinite (mean-field limit), finite complete graphs and finite sparse networks. For the latter case, we also derive the scaling exponent associated with the exponential growth of switching times as a function of the distance from the critical point. PMID:22448238

Social support and social network as intermediary social determinants of dental caries in adolescents.

PubMed

Fontanini, Humberto; Marshman, Zoe; Vettore, Mario

2015-04-01

The aim of this study was to investigate the association between intermediary social determinants, namely social support and social network with dental caries in adolescents. An adapted version of the WHO social determinants of health conceptual framework was used to organize structural and intermediary social determinants of dental caries into six blocks including perceived social support and number of social networks. A cross-sectional study was conducted with a representative sample of 542 students between 12 and 14 years of age in public schools located in the city of Dourados, Brazil in 2012. The outcome variables were caries experience (DMFT ≥ 1) and current dental caries (component D of DMFT ≥ 1) recorded by a calibrated dentist. Individual interviews were performed to collect data on perceived social support and numbers of social networks from family and friends and covariates. Multivariate Poisson regressions using hierarchical models were conducted. The prevalence of adolescents with caries experience and current dental caries was 55.2% and 32.1%, respectively. Adolescents with low numbers of social networks and low levels of social support from family (PR 1.47; 95% CI = 1.01-2.14) were more likely to have DMFT ≥ 1. Current dental caries was associated with low numbers of social networks and low levels of social support from family (PR 2.26; 95% CI = 1.15-4.44). Social support and social network were influential psychosocial factors to dental caries in adolescents. This finding requires confirmation in other countries but potentially has implications for programmes to promote oral health. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Merging Social Networking Environments and Formal Learning Environments to Support and Facilitate Interprofessional Instruction

PubMed Central

King, Sharla; Greidanus, Elaine; Carbonaro, Michael; Drummond, Jane; Patterson, Steven

2009-01-01

This study describes the redesign of an interprofessional team development course for health science students. A theoretical model is hypothesized as a framework for the redesign process, consisting of two themes: 1) the increasing trend among post-secondary students to participate in social networking (e.g., Facebook, Second Life) and 2) the need for healthcare educators to provide interprofessional training that results in effective communities of practice and better patient care. The redesign focused on increasing the relevance of the course through the integration of custom-designed technology to facilitate social networking during their interprofessional education. Results suggest that students in an educationally structured social networking environment can be guided to join learning communities quickly and access course materials. More research and implementation work is required to effectively develop interprofessional health sciences communities in a combined face-to-face and on-line social networking context. PMID:20165519

Merging social networking environments and formal learning environments to support and facilitate interprofessional instruction.

PubMed

King, Sharla; Greidanus, Elaine; Carbonaro, Michael; Drummond, Jane; Patterson, Steven

2009-04-28

This study describes the redesign of an interprofessional team development course for health science students. A theoretical model is hypothesized as a framework for the redesign process, consisting of two themes: 1) the increasing trend among post-secondary students to participate in social networking (e.g., Facebook, Second Life) and 2) the need for healthcare educators to provide interprofessional training that results in effective communities of practice and better patient care. The redesign focused on increasing the relevance of the course through the integration of custom-designed technology to facilitate social networking during their interprofessional education. Results suggest that students in an educationally structured social networking environment can be guided to join learning communities quickly and access course materials. More research and implementation work is required to effectively develop interprofessional health sciences communities in a combined face-to-face and on-line social networking context.

Unfavorable Individuals in Social Gaming Networks.

PubMed

Zhang, Yichao; Chen, Guanrong; Guan, Jihong; Zhang, Zhongzhi; Zhou, Shuigeng

2015-12-09

In social gaming networks, the current research focus has been on the origin of widespread reciprocal behaviors when individuals play non-cooperative games. In this paper, we investigate the topological properties of unfavorable individuals in evolutionary games. The unfavorable individuals are defined as the individuals gaining the lowest average payoff in a round of game. Since the average payoff is normally considered as a measure of fitness, the unfavorable individuals are very likely to be eliminated or change their strategy updating rules from a Darwinian perspective. Considering that humans can hardly adopt a unified strategy to play with their neighbors, we propose a divide-and-conquer game model, where individuals can interact with their neighbors in the network with appropriate strategies. We test and compare a series of highly rational strategy updating rules. In the tested scenarios, our analytical and simulation results surprisingly reveal that the less-connected individuals in degree-heterogeneous networks are more likely to become the unfavorable individuals. Our finding suggests that the connectivity of individuals as a social capital fundamentally changes the gaming environment. Our model, therefore, provides a theoretical framework for further understanding the social gaming networks.

Unfavorable Individuals in Social Gaming Networks

NASA Astrophysics Data System (ADS)

Zhang, Yichao; Chen, Guanrong; Guan, Jihong; Zhang, Zhongzhi; Zhou, Shuigeng

2015-12-01

In social gaming networks, the current research focus has been on the origin of widespread reciprocal behaviors when individuals play non-cooperative games. In this paper, we investigate the topological properties of unfavorable individuals in evolutionary games. The unfavorable individuals are defined as the individuals gaining the lowest average payoff in a round of game. Since the average payoff is normally considered as a measure of fitness, the unfavorable individuals are very likely to be eliminated or change their strategy updating rules from a Darwinian perspective. Considering that humans can hardly adopt a unified strategy to play with their neighbors, we propose a divide-and-conquer game model, where individuals can interact with their neighbors in the network with appropriate strategies. We test and compare a series of highly rational strategy updating rules. In the tested scenarios, our analytical and simulation results surprisingly reveal that the less-connected individuals in degree-heterogeneous networks are more likely to become the unfavorable individuals. Our finding suggests that the connectivity of individuals as a social capital fundamentally changes the gaming environment. Our model, therefore, provides a theoretical framework for further understanding the social gaming networks.

Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

ERIC Educational Resources Information Center

Firdausiah Mansur, Andi Besse; Yusof, Norazah

2013-01-01

Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

Predicting Positive and Negative Relationships in Large Social Networks.

PubMed

Wang, Guan-Nan; Gao, Hui; Chen, Lian; Mensah, Dennis N A; Fu, Yan

2015-01-01

In a social network, users hold and express positive and negative attitudes (e.g. support/opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM). Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.

Fat-tailed fluctuations in the size of organizations: the role of social influence.

PubMed

Mondani, Hernan; Holme, Petter; Liljeros, Fredrik

2014-01-01

Organizational growth processes have consistently been shown to exhibit a fatter-than-Gaussian growth-rate distribution in a variety of settings. Long periods of relatively small changes are interrupted by sudden changes in all size scales. This kind of extreme events can have important consequences for the development of biological and socio-economic systems. Existing models do not derive this aggregated pattern from agent actions at the micro level. We develop an agent-based simulation model on a social network. We take our departure in a model by a Schwarzkopf et al. on a scale-free network. We reproduce the fat-tailed pattern out of internal dynamics alone, and also find that it is robust with respect to network topology. Thus, the social network and the local interactions are a prerequisite for generating the pattern, but not the network topology itself. We further extend the model with a parameter δ that weights the relative fraction of an individual's neighbours belonging to a given organization, representing a contextual aspect of social influence. In the lower limit of this parameter, the fraction is irrelevant and choice of organization is random. In the upper limit of the parameter, the largest fraction quickly dominates, leading to a winner-takes-all situation. We recover the real pattern as an intermediate case between these two extremes.

Young Men's Social Network Characteristics and Associations with Sexual Partnership Concurrency in Tanzania.

PubMed

Yamanis, Thespina J; Fisher, Jacob C; Moody, James W; Kajula, Lusajo J

2016-06-01

Social network influence on young people's sexual behavior is understudied in sub-Saharan Africa. Previous research identified networks of mostly young men in Dar es Salaam who socialize in "camps". This study describes network characteristics within camps and their relationship to young men's concurrent sexual partnerships. We conducted surveys with a nearly complete census of ten camp networks (490 men and 160 women). Surveys included name generators to identify camp-based networks. Fifty seven percent of sexually active men (n = 471) reported past year concurrency, measured using the UNAIDS method. In a multivariable model, men's individual concurrency was associated with being a member of a closer knit camp in which concurrency was the normative behavior. Younger men who had older members in their networks were more likely to engage in concurrency. Respondent concurrency was also associated with inequitable personal gender norms. Our findings suggest strategies for leveraging social networks for HIV prevention among young men.

High-risk sexual activity in the House and Ball community: influence of social networks.

PubMed

Schrager, Sheree M; Latkin, Carl A; Weiss, George; Kubicek, Katrina; Kipke, Michele D

2014-02-01

We investigated the roles of House membership and the influence of social and sexual network members on the sexual risk behavior of men in the Los Angeles House and Ball community. From February 2009 to January 2010, male participants (n = 233) completed interviewer-assisted surveys during a House meeting or Ball event. We used logistic regression to model the effects of sexual network size, influence of sexual network members, House membership status, and their interactions on high-risk sex. Significant predictors of high-risk sex included number of sexual partners in the nominated social network, multiethnicity, and previous diagnosis of sexually transmitted infection. House membership was protective against high-risk sex. Additionally, a 3-way interaction emerged between number of sexual partners in the network, influence, and network members' House membership. Future research should assess network members' attitudes and behavior in detail to provide a greater understanding of the dynamics of social influence and to identify additional avenues for intervention.

Do People Know I’m Poz?: Factors Associated with Knowledge of Serostatus among HIV-positive African Americans’ Social Network Members

PubMed Central

Hoover, Matthew A.; Green, Harold D.; Bogart, Laura M.; Wagner, Glenn J.; Mutchler, Matt G.; Galvan, Frank H.; McDavitt, Bryce

2015-01-01

We examined how functional social support, HIV-related discrimination, internalized HIV stigma, and social network structure and composition were cross-sectionally associated with network members’ knowledge of respondents’ serostatus among 244 HIV-positive African Americans in Los Angeles. Results of a generalized hierarchical linear model indicated people in respondents’ networks who were highly trusted, well-known to others (high degree centrality), HIV-positive, or sex partners were more likely to know respondents’ HIV serostatus; African American network members were less likely to know respondents’ serostatus, as were drug-using partners. Greater internalized stigma among respondents living with HIV was associated with less knowledge of their seropositivity within their social network whereas greater respondent-level HIV discrimination was associated with more knowledge of seropositivity within the network. Additional research is needed to understand the causal mechanisms and mediating processes associated with serostatus disclosure as well as the long-term consequences of disclosure and network members’ knowledge of respondents’ serostatus. PMID:25903505

Do People Know I'm Poz?: Factors Associated with Knowledge of Serostatus Among HIV-Positive African Americans' Social Network Members.

PubMed

Hoover, Matthew A; Green, Harold D; Bogart, Laura M; Wagner, Glenn J; Mutchler, Matt G; Galvan, Frank H; McDavitt, Bryce

2016-01-01

We examined how functional social support, HIV-related discrimination, internalized HIV stigma, and social network structure and composition were cross-sectionally associated with network members' knowledge of respondents' serostatus among 244 HIV-positive African Americans in Los Angeles. Results of a generalized hierarchical linear model indicated people in respondents' networks who were highly trusted, well-known to others (high degree centrality), HIV-positive, or sex partners were more likely to know respondents' HIV serostatus; African American network members were less likely to know respondents' serostatus, as were drug-using partners. Greater internalized stigma among respondents living with HIV was associated with less knowledge of their seropositivity within their social network whereas greater respondent-level HIV discrimination was associated with more knowledge of seropositivity within the network. Additional research is needed to understand the causal mechanisms and mediating processes associated with serostatus disclosure as well as the long-term consequences of disclosure and network members' knowledge of respondents' serostatus.

Resting state brain networks in the prairie vole.

PubMed

Ortiz, Juan J; Portillo, Wendy; Paredes, Raul G; Young, Larry J; Alcauter, Sarael

2018-01-19

Resting state functional magnetic resonance imaging (rsfMRI) has shown the hierarchical organization of the human brain into large-scale complex networks, referred as resting state networks. This technique has turned into a promising translational research tool after the finding of similar resting state networks in non-human primates, rodents and other animal models of great value for neuroscience. Here, we demonstrate and characterize the presence of resting states networks in Microtus ochrogaster, the prairie vole, an extraordinary animal model to study complex human-like social behavior, with potential implications for the research of normal social development, addiction and neuropsychiatric disorders. Independent component analysis of rsfMRI data from isoflurane-anestethized prairie voles resulted in cortical and subcortical networks, including primary motor and sensory networks, but also included putative salience and default mode networks. We further discuss how future research could help to close the gap between the properties of the large scale functional organization and the underlying neurobiology of several aspects of social cognition. These results contribute to the evidence of preserved resting state brain networks across species and provide the foundations to explore the use of rsfMRI in the prairie vole for basic and translational research.

Predicting the behavior of techno-social systems.

PubMed

Vespignani, Alessandro

2009-07-24

We live in an increasingly interconnected world of techno-social systems, in which infrastructures composed of different technological layers are interoperating within the social component that drives their use and development. Examples are provided by the Internet, the World Wide Web, WiFi communication technologies, and transportation and mobility infrastructures. The multiscale nature and complexity of these networks are crucial features in understanding and managing the networks. The accessibility of new data and the advances in the theory and modeling of complex networks are providing an integrated framework that brings us closer to achieving true predictive power of the behavior of techno-social systems.

Childhood ADHD Symptoms: Association with Parental Social Networks and Mental Health Service Use during Adolescence.

PubMed

Bussing, Regina; Meyer, Johanna; Zima, Bonnie T; Mason, Dana M; Gary, Faye A; Garvan, Cynthia Wilson

2015-09-22

This study examines the associations of childhood attention-deficit/hyperactivity disorder (ADHD) risk status with subsequent parental social network characteristics and caregiver strain in adolescence; and examines predictors of adolescent mental health service use. Baseline ADHD screening identified children at high risk (n = 207) and low risk (n = 167) for ADHD. At eight-year follow-up, parents reported their social network characteristics, caregiver strain, adolescents' psychopathology and mental health service utilization, whereas adolescents self-reported their emotional status and ADHD stigma perceptions. Analyses were conducted using ANOVAs and nested logistic regression modeling. Parents of youth with childhood ADHD reported support networks consisting of fewer spouses but more healthcare professionals, and lower levels of support than control parents. Caregiver strain increased with adolescent age and psychopathology. Increased parental network support, youth ADHD symptoms, and caregiver strain, but lower youth stigma perceptions were independently associated with increased service use. Raising children with ADHD appears to significantly impact parental social network experiences. Reduced spousal support and overall lower network support levels may contribute to high caregiver strain commonly reported among parents of ADHD youth. Parental social network experiences influence adolescent ADHD service use. With advances in social networking technology, further research is needed to elucidate ways to enhance caregiver support during ADHD care.

The impact of socio-technical communication styles on the diversity and innovation potential of global science collaboratories

DOE PAGES

Ozmen, Ozgur; Yilmaz, Levent; Smith, Jeffrey

2016-02-09

Emerging cyber-infrastructure tools are enabling scientists to transparently co-develop, share, and communicate about real-time diverse forms of knowledge artifacts. In these environments, communication preferences of scientists are posited as an important factor affecting innovation capacity and robustness of social and knowledge network structures. Scientific knowledge creation in such communities is called global participatory science (GPS). Recently, using agent-based modeling and collective action theory as a basis, a complex adaptive social communication network model (CollectiveInnoSim) is implemented. This work leverages CollectiveInnoSim implementing communication preferences of scientists. Social network metrics and knowledge production patterns are used as proxy metrics to infer innovationmore » potential of emergent knowledge and collaboration networks. The objective is to present the underlying communication dynamics of GPS in a form of computational model and delineate the impacts of various communication preferences of scientists on innovation potential of the collaboration network. Ultimately, the insight gained can help policy-makers to design GPS environments and promote innovation.« less

The impact of socio-technical communication styles on the diversity and innovation potential of global science collaboratories

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ozmen, Ozgur; Yilmaz, Levent; Smith, Jeffrey

Emerging cyber-infrastructure tools are enabling scientists to transparently co-develop, share, and communicate about real-time diverse forms of knowledge artifacts. In these environments, communication preferences of scientists are posited as an important factor affecting innovation capacity and robustness of social and knowledge network structures. Scientific knowledge creation in such communities is called global participatory science (GPS). Recently, using agent-based modeling and collective action theory as a basis, a complex adaptive social communication network model (CollectiveInnoSim) is implemented. This work leverages CollectiveInnoSim implementing communication preferences of scientists. Social network metrics and knowledge production patterns are used as proxy metrics to infer innovationmore » potential of emergent knowledge and collaboration networks. The objective is to present the underlying communication dynamics of GPS in a form of computational model and delineate the impacts of various communication preferences of scientists on innovation potential of the collaboration network. Ultimately, the insight gained can help policy-makers to design GPS environments and promote innovation.« less

Modelling opinion formation driven communities in social networks

NASA Astrophysics Data System (ADS)

Iñiguez, Gerardo; Barrio, Rafael A.; Kertész, János; Kaski, Kimmo K.

2011-09-01

In a previous paper we proposed a model to study the dynamics of opinion formation in human societies by a co-evolution process involving two distinct time scales of fast transaction and slower network evolution dynamics. In the transaction dynamics we take into account short range interactions as discussions between individuals and long range interactions to describe the attitude to the overall mood of society. The latter is handled by a uniformly distributed parameter α, assigned randomly to each individual, as quenched personal bias. The network evolution dynamics is realised by rewiring the societal network due to state variable changes as a result of transaction dynamics. The main consequence of this complex dynamics is that communities emerge in the social network for a range of values in the ratio between time scales. In this paper we focus our attention on the attitude parameter α and its influence on the conformation of opinion and the size of the resulting communities. We present numerical studies and extract interesting features of the model that can be interpreted in terms of social behaviour.

Emotions as infectious diseases in a large social network: the SISa model

PubMed Central

Hill, Alison L.; Rand, David G.; Nowak, Martin A.; Christakis, Nicholas A.

2010-01-01

Human populations are arranged in social networks that determine interactions and influence the spread of diseases, behaviours and ideas. We evaluate the spread of long-term emotional states across a social network. We introduce a novel form of the classical susceptible–infected–susceptible disease model which includes the possibility for ‘spontaneous’ (or ‘automatic’) infection, in addition to disease transmission (the SISa model). Using this framework and data from the Framingham Heart Study, we provide formal evidence that positive and negative emotional states behave like infectious diseases spreading across social networks over long periods of time. The probability of becoming content is increased by 0.02 per year for each content contact, and the probability of becoming discontent is increased by 0.04 per year per discontent contact. Our mathematical formalism allows us to derive various quantities from the data, such as the average lifetime of a contentment ‘infection’ (10 years) or discontentment ‘infection’ (5 years). Our results give insight into the transmissive nature of positive and negative emotional states. Determining to what extent particular emotions or behaviours are infectious is a promising direction for further research with important implications for social science, epidemiology and health policy. Our model provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviours, health states, ideas or diseases with reservoirs. PMID:20610424

Emotions as infectious diseases in a large social network: the SISa model.

PubMed

Hill, Alison L; Rand, David G; Nowak, Martin A; Christakis, Nicholas A

2010-12-22

Human populations are arranged in social networks that determine interactions and influence the spread of diseases, behaviours and ideas. We evaluate the spread of long-term emotional states across a social network. We introduce a novel form of the classical susceptible-infected-susceptible disease model which includes the possibility for 'spontaneous' (or 'automatic') infection, in addition to disease transmission (the SISa model). Using this framework and data from the Framingham Heart Study, we provide formal evidence that positive and negative emotional states behave like infectious diseases spreading across social networks over long periods of time. The probability of becoming content is increased by 0.02 per year for each content contact, and the probability of becoming discontent is increased by 0.04 per year per discontent contact. Our mathematical formalism allows us to derive various quantities from the data, such as the average lifetime of a contentment 'infection' (10 years) or discontentment 'infection' (5 years). Our results give insight into the transmissive nature of positive and negative emotional states. Determining to what extent particular emotions or behaviours are infectious is a promising direction for further research with important implications for social science, epidemiology and health policy. Our model provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviours, health states, ideas or diseases with reservoirs.

Qualitative analysis of social network influences on quitting smoking among individuals with serious mental illness.

PubMed

Aschbrenner, Kelly A; Naslund, John A; Gill, Lydia; Hughes, Terence; O'Malley, Alistair J; Bartels, Stephen J; Brunette, Mary F

2017-07-04

The prevalence of cigarette smoking among adults with serious mental illness (SMI) remains high in the United States despite the availability of effective smoking cessation treatment. Identifying social influences on smoking and smoking cessation may help enhance intervention strategies to help smokers with SMI quit. The objective of this qualitative study was to explore social network influences on efforts to quit smoking among adults with SMI enrolled in a cessation treatment program. Participants were 41 individuals with SMI enrolled in a Medicaid Demonstration Project of smoking cessation at community mental health centers. A convenience sampling strategy was used to recruit participants for social network interviews exploring the influence of family, friends, peers, and significant others on quitting smoking. A team-based analysis of qualitative data involved descriptive coding, grouping coded data into categories, and identifying themes across the data. Social barriers to quitting smoking included pro-smoking social norms, attitudes, and behaviors of social network members, and negative interactions with network members, either specific to smoking or that triggered smoking. Social facilitators to quitting included quitting with network members, having cessation role models, and social support for quitting from network members. Similar to the general population, social factors appear to influence efforts to quit smoking among individuals with SMI enrolled in cessation treatment. Interventions that leverage positive social influences on smoking cessation have the potential to enhance strategies to help individuals with SMI quit smoking.

Associations between social support network characteristics and receipt of emotional and material support among a sample of male sexual minority youth

PubMed Central

Kapadia, Farzana; Halkitis, Perry; Barton, Staci; Siconolfi, Daniel; Figueroa, Rafael Perez

2014-01-01

Few studies have examined how social support network characteristics are related to perceived receipt of social support among male sexual minority youth. Using egocentric network data collected from a study of male sexual minority youth (n=592), multivariable logistic regression analyses examined distinct associations between individual and social network characteristics with receipt of (1) emotional and (2) material support. In multivariable models, frequent communication and having friends in one’s network yielded a two-fold increase in the likelihood of receiving emotional support whereas frequent communication was associated with an almost three-fold higher likelihood of perceived material support. Finally, greater internalized homophobia and personal experiences of gay-related stigma were inversely associated with perceived receipt of emotional and material support, respectively. Understanding the evolving social context and social interactions of this new generation of male sexual minority youth is warranted in order to understand the broader, contextual factors associated with their overall health and well-being. PMID:25214756

Latino Civic Group Participation, Social Networks, and Physical Activity.

PubMed

Marquez, Becky; Gonzalez, Patricia; Gallo, Linda; Ji, Ming

2016-07-01

We examined whether social networks and resource awareness for physical activity may mediate the relationship between civic group participation and physical activity. This is a cross-sectional study of a randomly selected sample of 335 Latinos (mean age 42.1 ± 16.4 years) participating in the San Diego Prevention Research Center's 2009 Household Community Survey. Serial multiple mediation analysis tested the hypothesis that civic group participation is associated with meeting physical activity recommendations through an indirect mechanism of larger social networks followed by greater knowledge of physical activity community resources. The indirect effects of level of civic group participation as well as religious, health, neighborhood, or arts group participation on meeting national physical activity recommendations were significant in models testing pathways through social network size and physical activity resource awareness. The direct effect was only significant for health group indicating that participating in a health group predicted physical activity independent of social network size and awareness of physical activity resources. Belonging to civic groups may promote physical activity engagement through social network diffusion of information on community physical activity resources which has implications for health.

Selective Narrowing of Social Networks Across Adulthood is Associated With Improved Emotional Experience in Daily Life

PubMed Central

English, Tammy; Carstensen, Laura L.

2014-01-01

Past research has documented age differences in the size and composition of social networks that suggest that networks grow smaller with age and include an increasingly greater proportion of well-known social partners. According to socioemotional selectivity theory, such changes in social network composition serve an antecedent emotion regulatory function that supports an age-related increase in the priority that people place on emotional well-being. The present study employed a longitudinal design with a sample that spanned the full adult age range to examine whether there is evidence of within-individual (developmental) change in social networks and whether the characteristics of relationships predict emotional experiences in daily life. Using growth curve analyses, social networks were found to increase in size in young adulthood and then decline steadily throughout later life. As postulated by socioemotional selectivity theory, reductions were observed primarily in the number of peripheral partners; the number of close partners was relatively stable over time. In addition, cross-sectional analyses revealed that older adults reported that social network members elicited less negative emotion and more positive emotion. The emotional tone of social networks, particularly when negative emotions were associated with network members, also predicted experienced emotion of participants. Overall, findings were robust after taking into account demographic variables and physical health. The implications of these findings are discussed in the context of socioemotional selectivity theory and related theoretical models. PMID:24910483

A social network model for the development of a 'Theory of Mind'

NASA Astrophysics Data System (ADS)

Harré, Michael S.

2013-02-01

A "Theory of Mind" is one of the most important skills we as humans have developed; It enables us to infer the mental states and intentions of others, build stable networks of relationships and it plays a central role in our psychological make-up and development. Findings published earlier this year have also shown that we as a species as well as each of us individually benefit from the enlargement of the underlying neuro-anatomical regions that support our social networks, mediated by our Theory of Mind that stabilises these networks. On the basis of such progress and that of earlier work, this paper draws together several different strands from psychology, behavioural economics and network theory in order to generate a novel theoretical representation of the development of our social-cognition and how subsequent larger social networks enables much of our cultural development but at the increased risk of mental disorders.

Social network influences on initiation and maintenance of reduced drinking among college students.

PubMed

Reid, Allecia E; Carey, Kate B; Merrill, Jennifer E; Carey, Michael P

2015-02-01

To determine whether (a) social networks influence the extent to which college students initiate and/or maintain reductions in drinking following an alcohol intervention and (b) students with riskier networks respond better to a counselor-delivered, vs. a computer-delivered, intervention. Mandated students (N = 316; 63% male) provided their perceptions of peer network members' drinking statuses (e.g., heavy drinker) and how accepting each friend would be if the participant reduced his or her drinking. Next, they were randomized to receive a brief motivational intervention (BMI) or Alcohol Edu for Sanctions (EDU). In latent growth models controlling for baseline levels on outcomes, influences of social networks on 2 phases of intervention response were examined: initiation of reductions in drinks per heaviest week, peak blood alcohol content (BAC), and consequences at 1 month (model intercepts) and maintenance of reductions between 1 and 12 months (model slopes). Peer drinking status predicted initiation of reductions in drinks per heaviest week and peak BAC; peer acceptability predicted initial reductions in consequences. Peer Acceptability × Condition interactions were significant or marginal for all outcomes in the maintenance phase. In networks with higher perceived acceptability of decreasing use, BMI and EDU exhibited similar growth rates. In less accepting networks, growth rates were significantly steeper among EDU than BMI participants. For consumption outcomes, lower perceived peer acceptability predicted steeper rates of growth in drinking among EDU but not BMI participants. Understanding how social networks influence behavior change and how interventions mitigate their influence is important for optimizing efficacy of alcohol interventions. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Social networks help to infer causality in the tumor microenvironment.

PubMed

Crespo, Isaac; Doucey, Marie-Agnès; Xenarios, Ioannis

2016-03-15

Networks have become a popular way to conceptualize a system of interacting elements, such as electronic circuits, social communication, metabolism or gene regulation. Network inference, analysis, and modeling techniques have been developed in different areas of science and technology, such as computer science, mathematics, physics, and biology, with an active interdisciplinary exchange of concepts and approaches. However, some concepts seem to belong to a specific field without a clear transferability to other domains. At the same time, it is increasingly recognized that within some biological systems--such as the tumor microenvironment--where different types of resident and infiltrating cells interact to carry out their functions, the complexity of the system demands a theoretical framework, such as statistical inference, graph analysis and dynamical models, in order to asses and study the information derived from high-throughput experimental technologies. In this article we propose to adopt and adapt the concepts of influence and investment from the world of social network analysis to biological problems, and in particular to apply this approach to infer causality in the tumor microenvironment. We showed that constructing a bidirectional network of influence between cell and cell communication molecules allowed us to determine the direction of inferred regulations at the expression level and correctly recapitulate cause-effect relationships described in literature. This work constitutes an example of a transfer of knowledge and concepts from the world of social network analysis to biomedical research, in particular to infer network causality in biological networks. This causality elucidation is essential to model the homeostatic response of biological systems to internal and external factors, such as environmental conditions, pathogens or treatments.

Convoys of social support in Mexico: Examining socio-demographic variation.

PubMed

Fuller-Iglesias, Heather R; Antonucci, Toni

2016-07-01

The Convoy Model suggests that at different stages of the lifespan the makeup of the social support network varies in step with developmental and contextual needs. Cultural norms may shape the makeup of social convoys as well as denote socio-demographic differences in social support. This study examines the social convoys of adults in Mexico. Specifically, it examines whether social network structure varies by age, gender, and education level, thus addressing the paucity of research on interpersonal relations in Mexico. A sample of 1,202 adults (18-99 years of age) was drawn from the Study of Social Relations and Well-being in Mexico. Hierarchical regression analyses indicated older adults had larger, more geographically proximate networks with a greater proportion of kin but less frequent contact. Women had larger, less geographically proximate networks with less frequent contact. Less educated individuals had smaller, more geographically proximate networks with more frequent contact and a greater proportion of kin. Age moderated gender and education effects indicated that younger women have more diverse networks and less educated older adults have weaker social ties. This study highlights socio-demographic variation in social convoys within the Mexican context, and suggests implications for fostering intergenerational relationships, policy, and interventions. Future research on Mexican convoys should further explore sources of support, and specifically address implications for well-being.

Intra-Urban Movement Flow Estimation Using Location Based Social Networking Data

NASA Astrophysics Data System (ADS)

Kheiri, A.; Karimipour, F.; Forghani, M.

2015-12-01

In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook, which have attracted an increasing number of users and greatly enriched their urban experience. Location-based social network data, as a new travel demand data source, seems to be an alternative or complement to survey data in the study of mobility behavior and activity analysis because of its relatively high access and low cost. In this paper, three OD estimation models have been utilized in order to investigate their relative performance when using Location-Based Social Networking (LBSN) data. For this, the Foursquare LBSN data was used to analyze the intra-urban movement behavioral patterns for the study area, Manhattan, the most densely populated of the five boroughs of New York city. The outputs of models are evaluated using real observations based on different criterions including distance distribution, destination travel constraints. The results demonstrate the promising potential of using LBSN data for urban travel demand analysis and monitoring.

Aggression Norms in the Classroom Social Network: Contexts of Aggressive Behavior and Social Preference in Middle Childhood.

PubMed

Jackson, Daisy R; Cappella, Elise; Neal, Jennifer Watling

2015-12-01

In a cross-sectional sample of African-American 2nd-4th grade students (N = 681), we examine the moderating effects of classroom overt and relational aggression norms on peers' social acceptance of classmates who exhibit overt and relational aggression in urban schools. Extending theory and research on classroom norms, we integrate social network data to adjust aggression norms based on children's direct and indirect connections in the classroom. Results of multilevel models indicate that network-based classroom aggression norms moderated relations between children's aggressive behavior and their social preference. Specifically, children benefited socially when their form of aggressive behavior fit with what was normative in the classroom social context. The moderating effect of classroom aggression norms was stronger for the association between overt aggression and social preference than relational aggression and social preference. Relationally aggressive youth were socially preferred by peers regardless of the classroom norm, although this positive association was magnified in classrooms with higher levels of relational aggression. Future research focused on aggression norms within classroom social networks are discussed and implications for school prevention efforts are considered.

The power of social networks and social support in promotion of physical activity and body mass index among African American adults.

PubMed

Flórez, Karen R; Richardson, Andrea S; Ghosh-Dastidar, Madhumita Bonnie; Troxel, Wendy; DeSantis, Amy; Colabianchi, Natalie; Dubowitz, Tamara

2018-04-01

Social support and social networks can elucidate important structural and functional aspects of social relationships that are associated with health-promoting behaviors, including Physical Activity (PA) and weight. A growing number of studies have investigated the relationship between social support, social networks, PA and obesity specifically among African Americans; however, the evidence is mixed and many studies focus exclusively on African American women. Most studies have also focused on either functional or structural aspects of social relationships (but not both) and few have objectively measured moderate-to-vigorous physical activity (MVPA) and body mass index (BMI). Cross-sectional surveys of adult African American men and women living in two low-income predominantly African American neighborhoods in Pittsburgh, PA (N = 799) measured numerous structural features as well as functional aspects of social relationships. Specifically, structural features included social isolation, and social network size and diversity. Functional aspects included perceptions of social support for physical activity from the social network in general as well as from family and friends specifically. Height, weight, and PA were objectively measured. From these, we derived Body Mass Index (BMI) and moderate-to-vigorous physical activity (MVPA). All regression models were stratified by gender, and included age, income, education, employment, marital status, physical limitations, and a neighborhood indicator. Greater social isolation was a significant predictor of lower BMI among men only. Among women only, social isolation was significantly associated with increased MVPA whereas, network diversity was significantly associated with reduced MVPA. Future research would benefit from in-depth qualitative investigations to understand how social networks may act to influence different types of physical activity among African Americans, as well as understand how they can be possible levers for health promotion and prevention.

Leveraging social influence to address overweight and obesity using agent-based models: the role of adolescent social networks.

PubMed

Zhang, J; Tong, L; Lamberson, P J; Durazo-Arvizu, R A; Luke, A; Shoham, D A

2015-01-01

The prevalence of adolescent overweight and obesity (hereafter, simply "overweight") in the US has increased over the past several decades. Individually-targeted prevention and treatment strategies targeting individuals have been disappointing, leading some to propose leveraging social networks to improve interventions. We hypothesized that social network dynamics (social marginalization; homophily on body mass index, BMI) and the strength of peer influence would increase or decrease the proportion of network member (agents) becoming overweight over a simulated year, and that peer influence would operate differently in social networks with greater overweight. We built an agent-based model (ABM) using results from R-SIENA. ABMs allow for the exploration of potential interventions using simulated agents. Initial model specifications were drawn from Wave 1 of the National Longitudinal Study of Adolescent Health (Add Health). We focused on a single saturation school with complete network and BMI data over two waves (n = 624). The model was validated against empirical observations at Wave 2. We focused on overall overweight prevalence after a simulated year. Five experiments were conducted: (1) changing attractiveness of high-BMI agents; (2) changing homophily on BMI; (3) changing the strength of peer influence; (4) shifting the overall BMI distribution; and (5) targeting dietary interventions to highly connected individuals. Increasing peer influence showed a dramatic decrease in the prevalence of overweight; making peer influence negative (i.e., doing the opposite of friends) increased overweight. However, the effect of peer influence varied based on the underlying distribution of BMI; when BMI was increased overall, stronger peer influence increased proportion of overweight. Other interventions, including targeted dieting, had little impact. Peer influence may be a viable target in overweight interventions, but the distribution of body size in the population needs to be taken into account. In low-obesity populations, strengthening peer influence may be a useful strategy. Copyright © 2014 Elsevier Ltd. All rights reserved.

Leveraging social influence to address overweight and obesity using agent-based models: the role of adolescent social networks

PubMed Central

Zhang, J; Tong, L; Lamberson, PJ; Durazo, R; Luke, A; Shoham, DA

2014-01-01

The prevalence of adolescent overweight and obesity (hereafter, simply “overweight”) in the US has increased over the past several decades. Individually-targeted prevention and treatment strategies targeting individuals have been disappointing, leading some to propose leveraging social networks to improve interventions. We hypothesized that social network dynamics (social marginalization; homophily on body mass index, BMI) and the strength of peer influence would increase or decrease the proportion of network member (agents) becoming overweight over a simulated year, and that peer influence would operate differently in social networks with greater overweight. We built an agent-based model (ABM) using results from R-SIENA. ABMs allow for the exploration of potential interventions using simulated agents. Initial model specifications were drawn from Wave 1 of the National Longitudinal Study of Adolescent Health (Add Health). We focused on a single saturation school with complete network and BMI data over two waves (n=624). The model was validated against empirical observations at Wave 2. We focused on overall overweight prevalence after a simulated year. Five experiments were conducted: (1) changing attractiveness of high-BMI agents; (2) changing homophily on BMI; (3) changing the strength of peer influence; (4) shifting the overall BMI distribution; and (5) targeting dietary interventions to highly connected individuals. Increasing peer influence showed a dramatic decrease in the prevalence of overweight; making peer influence negative (ie, doing the opposite of friends) increased overweight. However, the effect of peer influence varied based on the underlying distribution of BMI; when BMI was increased overall, stronger peer influence increased proportion of overweight. Other interventions, including targeted dieting, had little impact. Peer influence may be a viable target in overweight interventions, but the distribution of body size in the population needs to be taken into account. In low-obesity populations, strengthening peer influence may be a useful strategy. PMID:24951404

Smoking Behavior and Friendship Formation: The Importance of Time Heterogeneity in Studying Social Network Dynamics

DTIC Science & Technology

2010-01-01

Smoking Behavior and Friendship Formation: The Importance of Time Heterogeneity in Studying Social Network Dynamics Joshua A. Lospinoso Department of...djsatchell@gmail.com Abstract—This study illustrates the importance of assessing and accounting for time heterogeneity in longitudinal social net- work...analysis. We apply the time heterogeneity model selection procedure of [1] to a dataset collected on social tie formation for university freshman in the

Why is social network drinking associated with college students' alcohol use? Focus on psychological mediators.

PubMed

Reid, Allecia E; Carey, Kate B

2018-06-01

Level of drinking in the social network is strongly associated with college students' alcohol use. However, mechanisms through which networks are associated with personal drinking have been underexplored thus far. The present study examined theoretically derived constructs-sociability outcome expectancies, attitudes toward heavy drinking, self-efficacy for use of protective strategies, and descriptive norms-as potential mediators of the association between egocentric social network drinking and personal consumption. College students (N = 274) self-reported their social network's level of alcohol consumption, all mediators, drinks per week, and consequences at both baseline (Time 1) and a 1-month follow-up (Time 2). Autoregressive mediation models focused on the longitudinal associations between Time 1 network drinking and the Time 2 mediators and between the Time 1 mediators and the Time 2 outcomes. Consistent with hypotheses, Time 1 social network drinking was significantly associated with Time 2 drinks per week and consequences. Only attitudes significantly mediated social network associations with drinks per week and consequences, though the proportion of the total effects accounted for by attitudes was small. After accounting for the stability of constructs over time, social network drinking was generally un- or weakly related to sociability expectancies, self-efficacy, and descriptive norms. Results support reducing attitudes toward heavy drinking as a potential avenue for mitigating network effects, but also highlight the need to evaluate additional potential mechanisms of network effects. Intervention efforts that aim to address the social network have the potential to substantially reduce alcohol consumption, thereby enhancing the overall efficacy of alcohol risk-reduction interventions. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Analysing Health Professionals' Learning Interactions in an Online Social Network: A Longitudinal Study.

PubMed

Li, Xin; Verspoor, Karin; Gray, Kathleen; Barnett, Stephen

2016-01-01

This paper summarises a longitudinal analysis of learning interactions occurring over three years among health professionals in an online social network. The study employs the techniques of Social Network Analysis (SNA) and statistical modeling to identify the changes in patterns of interaction over time and test associated structural network effects. SNA results indicate overall low participation in the network, although some participants became active over time and even led discussions. In particular, the analysis has shown that a change of lead contributor results in a change in learning interaction and network structure. The analysis of structural network effects demonstrates that the interaction dynamics slow down over time, indicating that interactions in the network are more stable. The health professionals may be reluctant to share knowledge and collaborate in groups but were interested in building personal learning networks or simply seeking information.

A social network-informed latent class analysis of patterns of substance use, sexual behavior, and mental health: Social Network Study III, Winnipeg, Manitoba, Canada.

PubMed

Hopfer, Suellen; Tan, Xianming; Wylie, John L

2014-05-01

We assessed whether a meaningful set of latent risk profiles could be identified in an inner-city population through individual and network characteristics of substance use, sexual behaviors, and mental health status. Data came from 600 participants in Social Network Study III, conducted in 2009 in Winnipeg, Manitoba, Canada. We used latent class analysis (LCA) to identify risk profiles and, with covariates, to identify predictors of class. A 4-class model of risk profiles fit the data best: (1) solitary users reported polydrug use at the individual level, but low probabilities of substance use or concurrent sexual partners with network members; (2) social-all-substance users reported polydrug use at the individual and network levels; (3) social-noninjection drug users reported less likelihood of injection drug and solvent use; (4) low-risk users reported low probabilities across substances. Unstable housing, preadolescent substance use, age, and hepatitis C status predicted risk profiles. Incorporation of social network variables into LCA can distinguish important subgroups with varying patterns of risk behaviors that can lead to sexually transmitted and bloodborne infections.

Social Relations in Lebanon: Convoys Across the Life Course

PubMed Central

Antonucci, Toni C.; Ajrouch, Kristine J.; Abdulrahim, Sawsan

2015-01-01

Objectives: This study systematically analyzed convoys of social relations to investigate the ways in which gender and income shape patterns of social relations across the life course in Lebanon. Methods: Data were drawn from a representative sample of adults aged 18 and older in Greater Beirut, Lebanon (N = 500). Multiple linear regression and multilevel models were conducted to examine main and interactive effects of age, gender, and income on social relations. Results: Findings indicate main effects of age, income, and gender on network structure and relationship quality. Older age was associated with larger network size, greater proportion of kin in network, higher positive and lower negative relationship quality. Higher income was associated with larger network size and decreased contact frequency. Female gender was also associated with decreased contact frequency. Gender interacted with income to influence network size and network composition. Higher income was associated with a larger network size and higher proportion of kin for women. Discussion: Findings suggest diversity in the experience of social relations. Such nuance is particularly relevant to the Lebanese context where family is the main source of support in old age. Policy makers and program planners may need to refrain from viewing social relations simplistically. PMID:24501252

Interests diffusion in social networks

NASA Astrophysics Data System (ADS)

D'Agostino, Gregorio; D'Antonio, Fulvio; De Nicola, Antonio; Tucci, Salvatore

2015-10-01

We provide a model for diffusion of interests in Social Networks (SNs). We demonstrate that the topology of the SN plays a crucial role in the dynamics of the individual interests. Understanding cultural phenomena on SNs and exploiting the implicit knowledge about their members is attracting the interest of different research communities both from the academic and the business side. The community of complexity science is devoting significant efforts to define laws, models, and theories, which, based on acquired knowledge, are able to predict future observations (e.g. success of a product). In the mean time, the semantic web community aims at engineering a new generation of advanced services by defining constructs, models and methods, adding a semantic layer to SNs. In this context, a leapfrog is expected to come from a hybrid approach merging the disciplines above. Along this line, this work focuses on the propagation of individual interests in social networks. The proposed framework consists of the following main components: a method to gather information about the members of the social networks; methods to perform some semantic analysis of the Domain of Interest; a procedure to infer members' interests; and an interests evolution theory to predict how the interests propagate in the network. As a result, one achieves an analytic tool to measure individual features, such as members' susceptibilities and authorities. Although the approach applies to any type of social network, here it is has been tested against the computer science research community. The DBLP (Digital Bibliography and Library Project) database has been elected as test-case since it provides the most comprehensive list of scientific production in this field.

Modeling online social signed networks

NASA Astrophysics Data System (ADS)

Li, Le; Gu, Ke; Zeng, An; Fan, Ying; Di, Zengru

2018-04-01

People's online rating behavior can be modeled by user-object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user-object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user's signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

Dynamical influence processes on networks: general theory and applications to social contagion.

PubMed

Harris, Kameron Decker; Danforth, Christopher M; Dodds, Peter Sheridan

2013-08-01

We study binary state dynamics on a network where each node acts in response to the average state of its neighborhood. By allowing varying amounts of stochasticity in both the network and node responses, we find different outcomes in random and deterministic versions of the model. In the limit of a large, dense network, however, we show that these dynamics coincide. We construct a general mean-field theory for random networks and show this predicts that the dynamics on the network is a smoothed version of the average response function dynamics. Thus, the behavior of the system can range from steady state to chaotic depending on the response functions, network connectivity, and update synchronicity. As a specific example, we model the competing tendencies of imitation and nonconformity by incorporating an off-threshold into standard threshold models of social contagion. In this way, we attempt to capture important aspects of fashions and societal trends. We compare our theory to extensive simulations of this "limited imitation contagion" model on Poisson random graphs, finding agreement between the mean-field theory and stochastic simulations.

The development of computer networks: First results from a microeconomic model

NASA Astrophysics Data System (ADS)

Maier, Gunther; Kaufmann, Alexander

Computer networks like the Internet are gaining importance in social and economic life. The accelerating pace of the adoption of network technologies for business purposes is a rather recent phenomenon. Many applications are still in the early, sometimes even experimental, phase. Nevertheless, it seems to be certain that networks will change the socioeconomic structures we know today. This is the background for our special interest in the development of networks, in the role of spatial factors influencing the formation of networks, and consequences of networks on spatial structures, and in the role of externalities. This paper discusses a simple economic model - based on a microeconomic calculus - that incorporates the main factors that generate the growth of computer networks. The paper provides analytic results about the generation of computer networks. The paper discusses (1) under what conditions economic factors will initiate the process of network formation, (2) the relationship between individual and social evaluation, and (3) the efficiency of a network that is generated based on economic mechanisms.

A novel game theoretic approach for modeling competitive information diffusion in social networks with heterogeneous nodes

NASA Astrophysics Data System (ADS)

Agha Mohammad Ali Kermani, Mehrdad; Fatemi Ardestani, Seyed Farshad; Aliahmadi, Alireza; Barzinpour, Farnaz

2017-01-01

Influence maximization deals with identification of the most influential nodes in a social network given an influence model. In this paper, a game theoretic framework is developed that models a competitive influence maximization problem. A novel competitive influence model is additionally proposed that incorporates user heterogeneity, message content, and network structure. The proposed game-theoretic model is solved using Nash Equilibrium in a real-world dataset. It is shown that none of the well-known strategies are stable and at least one player has the incentive to deviate from the proposed strategy. Moreover, violation of Nash equilibrium strategy by each player leads to their reduced payoff. Contrary to previous works, our results demonstrate that graph topology, as well as the nodes' sociability and initial tendency measures have an effect on the determination of the influential node in the network.

Multi-stage complex contagions.

PubMed

Melnik, Sergey; Ward, Jonathan A; Gleeson, James P; Porter, Mason A

2013-03-01

The spread of ideas across a social network can be studied using complex contagion models, in which agents are activated by contact with multiple activated neighbors. The investigation of complex contagions can provide crucial insights into social influence and behavior-adoption cascades on networks. In this paper, we introduce a model of a multi-stage complex contagion on networks. Agents at different stages-which could, for example, represent differing levels of support for a social movement or differing levels of commitment to a certain product or idea-exert different amounts of influence on their neighbors. We demonstrate that the presence of even one additional stage introduces novel dynamical behavior, including interplay between multiple cascades, which cannot occur in single-stage contagion models. We find that cascades-and hence collective action-can be driven not only by high-stage influencers but also by low-stage influencers.

Multi-stage complex contagions

NASA Astrophysics Data System (ADS)

Melnik, Sergey; Ward, Jonathan A.; Gleeson, James P.; Porter, Mason A.

2013-03-01

The spread of ideas across a social network can be studied using complex contagion models, in which agents are activated by contact with multiple activated neighbors. The investigation of complex contagions can provide crucial insights into social influence and behavior-adoption cascades on networks. In this paper, we introduce a model of a multi-stage complex contagion on networks. Agents at different stages—which could, for example, represent differing levels of support for a social movement or differing levels of commitment to a certain product or idea—exert different amounts of influence on their neighbors. We demonstrate that the presence of even one additional stage introduces novel dynamical behavior, including interplay between multiple cascades, which cannot occur in single-stage contagion models. We find that cascades—and hence collective action—can be driven not only by high-stage influencers but also by low-stage influencers.

Social Network and Mental Health Among Older Adults in Rural Uttar Pradesh, India: A Cross-Sectional Study.

PubMed

Singh, Lucky; Singh, Prashant Kumar; Arokiasamy, Perianayagam

2016-06-01

The rapid growth of the older population in India draws attention to the factors that contribute to their changing health realities. However, there has hardly been any study in India that has looked at the effects of specific social networks with children, relatives, friends and confidant on depression among older adults. The objective of the study is to investigate the association between social network and depression among the rural elderly. The study population comprised over 630 older adults (aged 60 and above) from the rural areas of Varanasi, Uttar Pradesh. We adopted Berkman's theoretical model of the impact of social relations on depression among the elderly in the Indian context. Results of the Confirmatory Factor Analysis (CFA) demonstrated that the four specific social network types: children, relatives, friends and confidant were tenable. The results showed that a better social network with 'friends/neighbours' was protective against depression among the rural elderly. This clearly points to the need for more social network centres for older adults, so that they can interact with friends within the community or between communities and participate in group activities.

Social pathways to health: On the mediating role of the social network in the relation between socio-economic position and health.

PubMed

Aartsen, Marja; Veenstra, Marijke; Hansen, Thomas

2017-12-01

Good health is one of the key qualities of life, but opportunities to be and remain healthy are unequally distributed across socio-economic groups. The beneficial health effects of the social network are well known. However, research on the social network as potential mediator in the pathway from socio-economic position (SEP) to health is scarce, while there are good reasons to expect a socio-economical patterning of networks. We aim to contribute to our understanding of socio-economic inequalities in health by examining the mediating role of structural and functional characteristics of the social network in the SEP-health relationship. Data were from the second wave of the Norwegian study on the life course, aging and generation study (NorLAG) and comprised 4534 men and 4690 women aged between 40 and 81. We applied multiple mediation models to evaluate the relative importance of each network characteristic, and multiple group analysis to examine differences between middle-aged and older men and women. Our results indicated a clear socio-economical patterning of the social network for men and women. People with higher SEP had social networks that better protect against loneliness, which in turn lead to better health outcomes. The explained variance in health in older people by the social network and SEP was only half of the explained variance observed in middle-aged people, suggesting that other factors than SEP were more important for health when people age. We conclude that it is the function of the network, rather than the structure, that counts for health.

Using the relational event model (REM) to investigate the temporal dynamics of animal social networks.

PubMed

Tranmer, Mark; Marcum, Christopher Steven; Morton, F Blake; Croft, Darren P; de Kort, Selvino R

2015-03-01

Social dynamics are of fundamental importance in animal societies. Studies on nonhuman animal social systems often aggregate social interaction event data into a single network within a particular time frame. Analysis of the resulting network can provide a useful insight into the overall extent of interaction. However, through aggregation, information is lost about the order in which interactions occurred, and hence the sequences of actions over time. Many research hypotheses relate directly to the sequence of actions, such as the recency or rate of action, rather than to their overall volume or presence. Here, we demonstrate how the temporal structure of social interaction sequences can be quantified from disaggregated event data using the relational event model (REM). We first outline the REM, explaining why it is different from other models for longitudinal data, and how it can be used to model sequences of events unfolding in a network. We then discuss a case study on the European jackdaw, Corvus monedula , in which temporal patterns of persistence and reciprocity of action are of interest, and present and discuss the results of a REM analysis of these data. One of the strengths of a REM analysis is its ability to take into account different ways in which data are collected. Having explained how to take into account the way in which the data were collected for the jackdaw study, we briefly discuss the application of the model to other studies. We provide details of how the models may be fitted in the R statistical software environment and outline some recent extensions to the REM framework.

Online Actions with Offline Impact: How Online Social Networks Influence Online and Offline User Behavior.

PubMed

Althoff, Tim; Jindal, Pranav; Leskovec, Jure

2017-02-01

Many of today's most widely used computing applications utilize social networking features and allow users to connect, follow each other, share content, and comment on others' posts. However, despite the widespread adoption of these features, there is little understanding of the consequences that social networking has on user retention, engagement, and online as well as offline behavior. Here, we study how social networks influence user behavior in a physical activity tracking application. We analyze 791 million online and offline actions of 6 million users over the course of 5 years, and show that social networking leads to a significant increase in users' online as well as offline activities. Specifically, we establish a causal effect of how social networks influence user behavior. We show that the creation of new social connections increases user online in-application activity by 30%, user retention by 17%, and user offline real-world physical activity by 7% (about 400 steps per day). By exploiting a natural experiment we distinguish the effect of social influence of new social connections from the simultaneous increase in user's motivation to use the app and take more steps. We show that social influence accounts for 55% of the observed changes in user behavior, while the remaining 45% can be explained by the user's increased motivation to use the app. Further, we show that subsequent, individual edge formations in the social network lead to significant increases in daily steps. These effects diminish with each additional edge and vary based on edge attributes and user demographics. Finally, we utilize these insights to develop a model that accurately predicts which users will be most influenced by the creation of new social network connections.

Online Actions with Offline Impact: How Online Social Networks Influence Online and Offline User Behavior

PubMed Central

Althoff, Tim; Jindal, Pranav; Leskovec, Jure

2017-01-01

Many of today’s most widely used computing applications utilize social networking features and allow users to connect, follow each other, share content, and comment on others’ posts. However, despite the widespread adoption of these features, there is little understanding of the consequences that social networking has on user retention, engagement, and online as well as offline behavior. Here, we study how social networks influence user behavior in a physical activity tracking application. We analyze 791 million online and offline actions of 6 million users over the course of 5 years, and show that social networking leads to a significant increase in users’ online as well as offline activities. Specifically, we establish a causal effect of how social networks influence user behavior. We show that the creation of new social connections increases user online in-application activity by 30%, user retention by 17%, and user offline real-world physical activity by 7% (about 400 steps per day). By exploiting a natural experiment we distinguish the effect of social influence of new social connections from the simultaneous increase in user’s motivation to use the app and take more steps. We show that social influence accounts for 55% of the observed changes in user behavior, while the remaining 45% can be explained by the user’s increased motivation to use the app. Further, we show that subsequent, individual edge formations in the social network lead to significant increases in daily steps. These effects diminish with each additional edge and vary based on edge attributes and user demographics. Finally, we utilize these insights to develop a model that accurately predicts which users will be most influenced by the creation of new social network connections. PMID:28345078

Coarse cluster enhancing collaborative recommendation for social network systems

NASA Astrophysics Data System (ADS)

Zhao, Yao-Dong; Cai, Shi-Min; Tang, Ming; Shang, Min-Sheng

2017-10-01

Traditional collaborative filtering based recommender systems for social network systems bring very high demands on time complexity due to computing similarities of all pairs of users via resource usages and annotation actions, which thus strongly suppresses recommending speed. In this paper, to overcome this drawback, we propose a novel approach, namely coarse cluster that partitions similar users and associated items at a high speed to enhance user-based collaborative filtering, and then develop a fast collaborative user model for the social tagging systems. The experimental results based on Delicious dataset show that the proposed model is able to dramatically reduce the processing time cost greater than 90 % and relatively improve the accuracy in comparison with the ordinary user-based collaborative filtering, and is robust for the initial parameter. Most importantly, the proposed model can be conveniently extended by introducing more users' information (e.g., profiles) and practically applied for the large-scale social network systems to enhance the recommending speed without accuracy loss.

Structural diversity effect on hashtag adoption in Twitter

NASA Astrophysics Data System (ADS)

Zhang, Aihua; Zheng, Mingxing; Pang, Bowen

2018-03-01

With online social network developing rapidly these years, user' behavior in online social network has attracted a lot of attentions to it. In this paper, we study Twitter user's behavior of hashtag adoption from the perspective of social contagion and focus on "structure diversity" effect on individual's behavior in Twitter. We achieve data through Twitter's API by crawling and build a users' network to carry on empirical research. The Girvan-Newman (G-N) algorithm is used to analyze the structural diversity of user's ego network, and Logistic regression model is adopted to examine the hypothesis. The findings of our empirical study indicate that user' behavior in online social network is indeed influenced by his friends and his decision is significantly affected by the number of groups that these friends belong to, which we call structural diversity.

Assessment of multiple constructs of social integration for older adults living in nursing homes.

PubMed

Leedahl, Skye N; Sellon, Alicia; Chapin, Rosemary K

2018-07-01

A variety of terms and measures have been used in the literature to denote being socially integrated, and many studies of older adults focus on only social networks or social support and often only include those living in the community. The purpose of this study was to assess multiple constructs of social integration (i.e., social networks, social capital, social support, and social engagement) for older adults in nursing homes. Data were collected from 140 older adults at 30 nursing homes in Kansas. We interviewed older adults' in-person using a survey questionnaire, and used multilevel confirmatory factor analysis to analyze the data. The final model that included the four constructs had acceptable fit (χ 2  = 174.71; df = 112; p < .01; CFI = .93; RMSEA = .06; SRMR-W = .06; SRMR-B = .12). The results showed that the proposed model was supported at the individual level. At the between-level, social networks and social support were supported. Study results have methodological and practice/policy implications for the study of older adults in long term care settings. In particular, this study contributes to understanding how to operationally define and differentiate social integration variables in studies of older adults, particularly when study data are hierarchical.

Evolution of individual versus social learning on social networks

PubMed Central

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-01-01

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of ‘cultural models’ exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. PMID:25631568

Disease implications of animal social network structure: A synthesis across social systems.

PubMed

Sah, Pratha; Mann, Janet; Bansal, Shweta

2018-05-01

The disease costs of sociality have largely been understood through the link between group size and transmission. However, infectious disease spread is driven primarily by the social organization of interactions in a group and not its size. We used statistical models to review the social network organization of 47 species, including mammals, birds, reptiles, fish and insects by categorizing each species into one of three social systems, relatively solitary, gregarious and socially hierarchical. Additionally, using computational experiments of infection spread, we determined the disease costs of each social system. We find that relatively solitary species have large variation in number of social partners, that socially hierarchical species are the least clustered in their interactions, and that social networks of gregarious species tend to be the most fragmented. However, these structural differences are primarily driven by weak connections, which suggest that different social systems have evolved unique strategies to organize weak ties. Our synthetic disease experiments reveal that social network organization can mitigate the disease costs of group living for socially hierarchical species when the pathogen is highly transmissible. In contrast, highly transmissible pathogens cause frequent and prolonged epidemic outbreaks in gregarious species. We evaluate the implications of network organization across social systems despite methodological challenges, and our findings offer new perspective on the debate about the disease costs of group living. Additionally, our study demonstrates the potential of meta-analytic methods in social network analysis to test ecological and evolutionary hypotheses on cooperation, group living, communication and resilience to extrinsic pressures. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

Engineering Online and In-Person Social Networks for Physical Activity: A Randomized Trial.

PubMed

Rovniak, Liza S; Kong, Lan; Hovell, Melbourne F; Ding, Ding; Sallis, James F; Ray, Chester A; Kraschnewski, Jennifer L; Matthews, Stephen A; Kiser, Elizabeth; Chinchilli, Vernon M; George, Daniel R; Sciamanna, Christopher N

2016-12-01

Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. The purpose of this study was to conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively measured outcomes. Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3 % male, 83.4 % overweight/obese) were randomized to one of three groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking as well as prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Participants increased their MVPA by 21.0 min/week, 95 % CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. The trial was registered with the ClinicalTrials.gov (NCT01142804).

The physics of teams: Interdependence, measurable entropy and computational emotion

NASA Astrophysics Data System (ADS)

Lawless, William F.

2017-08-01

Most of the social sciences, including psychology, economics and subjective social network theory, are modeled on the individual, leaving the field not only a-theoretical, but also inapplicable to a physics of hybrid teams, where hybrid refers to arbitrarily combining humans, machines and robots into a team to perform a dedicated mission (e.g., military, business, entertainment) or to solve a targeted problem (e.g., with scientists, engineers, entrepreneurs). As a common social science practice, the ingredient at the heart of the social interaction, interdependence, is statistically removed prior to the replication of social experiments; but, as an analogy, statistically removing social interdependence to better study the individual is like statistically removing quantum effects as a complication to the study of the atom. Further, in applications of Shannon’s information theory to teams, the effects of interdependence are minimized, but even there, interdependence is how classical information is transmitted. Consequently, numerous mistakes are made when applying non-interdependent models to policies, the law and regulations, impeding social welfare by failing to exploit the power of social interdependence. For example, adding redundancy to human teams is thought by subjective social network theorists to improve the efficiency of a network, easily contradicted by our finding that redundancy is strongly associated with corruption in non-free markets. Thus, built atop the individual, most of the social sciences, economics and social network theory have little if anything to contribute to the engineering of hybrid teams. In defense of the social sciences, the mathematical physics of interdependence is elusive, non-intuitive and non-rational. However, by replacing determinism with bistable states, interdependence at the social level mirrors entanglement at the quantum level, suggesting the applicability of quantum tools for social science. We report how our quantum-like models capture some of the essential aspects of interdependence, a tool for the metrics of hybrid teams; as an example, we find additional support for our model of the solution to the open problem of team size. We also report on progress with the theory of computational emotion for hybrid teams, linking it qualitatively to the second law of thermodynamics. We conclude that the science of interdependence

Optimization-Based Selection of Influential Agents in a Rural Afghan Social Network

DTIC Science & Technology

2010-06-01

nonlethal targeting model, a nonlinear programming ( NLP ) optimization formulation that identifies the k US agent assignment strategy producing the greatest...leader social network, and 3) the nonlethal targeting model, a nonlinear programming ( NLP ) optimization formulation that identifies the k US agent...NATO Coalition in Afghanistan. 55 for Afghanistan ( [54], [31], [48], [55], [30]). While Arab tribes tend to be more hierarchical, Pashtun tribes are

Social identity, social networks and recovery capital in emerging adulthood: A pilot study.

PubMed

Mawson, E; Best, D; Beckwith, M; Dingle, G A; Lubman, D I

2015-11-11

It has been argued that recovery from substance dependence relies on a change in identity, with past research focused on 'personal identity'. This study assessed support for a social identity model of recovery in emerging adults through examining associations between social identity, social networks, recovery capital, and quality of life. Twenty participants aged 18-21 in residential treatment for substance misuse were recruited from four specialist youth drug treatment services - three detoxification facilities and one psychosocial rehabilitation facility in Victoria, Australia. Participants completed a detailed social network interview exploring the substance use of groups in their social networks and measures of quality of life, recovery capital, and social identity. Lower group substance use was associated with higher recovery capital, stronger identification with non-using groups, and greater importance of non-using groups in the social network. Additionally, greater identification with and importance of non-using groups were associated with better environmental quality of life, whereas greater importance conferred on using groups was associated with reduced environmental quality of life. Support was found for the role of social identity processes in reported recovery capital and quality of life. Future research in larger, longitudinal samples is required to improve understanding of social identity processes during treatment and early recovery and its relationship to recovery stability.

Gender-related differences in the multi-pathway effect of social determinants on quality of life in older age-the COURAGE in Europe project.

PubMed

Tobiasz-Adamczyk, Beata; Galas, Aleksander; Zawisza, Katarzyna; Chatterji, Somnath; Haro, Josep Maria; Ayuso-Mateos, José Luis; Koskinen, Seppo; Leonardi, Matilde

2017-07-01

Gender-related differences in life expectancy, prevalence of chronic conditions and level of disability in the process of ageing have been broadly described. Less is known about social determinants, which may have different impacts on quality of life in men and women. The investigation aims to reveal gender-related differences in social determinants on quality of life assessed by a multi-pathway model including health, social, demographic and living place characteristics. The study group consisted of 5099 participants aged 50+ representing general populations of three different European regions (Finland, Poland, Spain) who participated in COURAGE in EUROPE Project. Standardized tools were used to measure quality of life (WHOQOL-AGE) and social determinants (COURAGE Social Network Index, OSLO-3 Social Support Scale, UCLA Loneliness Scale, participation scale and trust). A multipath model considering exogenous predictors (demographic, economic), mediators (social) and endogenous outcome (QOL) was created to reveal the role of determinants. Gender-related differences were investigated across three age categories: 50-64; 65-79 and 80+. The model (RMSEA = 0.058; CFI = 0.939) showed the effects of all of the investigated determinants. Gender-related differences in the association between social constructs and QOL were observed for social networks in the group of 80+, for social support in the group of 50-64 and 65-79 years, and for social participation in the group of 65-79 years. Males benefited more (in QOL) from social networks and social support, and women from social participation. The research provides valuable knowledge about the role of social determinants in QOL considering complex relations between different social constructs. Additionally, the results showed gender-related differences in the associations between social networks, social support, social participation and QOL, suggesting that men might benefit more from the interventions in the first two. Although our research did not investigate the effects of interventions, the results show directions for future investigations, how to shape social interventions at the population level to improve quality of life of older adults, and thus help achieve successful ageing.

Social networks, social support mechanisms, and quality of life after breast cancer diagnosis.

PubMed

Kroenke, Candyce H; Kwan, Marilyn L; Neugut, Alfred I; Ergas, Isaac J; Wright, Jaime D; Caan, Bette J; Hershman, Dawn; Kushi, Lawrence H

2013-06-01

We examined mechanisms through which social relationships influence quality of life (QOL) in breast cancer survivors. This study included 3,139 women from the Pathways Study who were diagnosed with breast cancer from 2006 to 2011 and provided data on social networks (the presence of a spouse or intimate partner, religious/social ties, volunteering, and numbers of close friends and relatives), social support (tangible support, emotional/informational support, affection, positive social interaction), and QOL, measured by the FACT-B, approximately 2 months post diagnosis. We used logistic models to evaluate associations between social network size, social support, and lower versus higher than median QOL scores. We further stratified by stage at diagnosis and treatment. In multivariate-adjusted analyses, women who were characterized as socially isolated had significantly lower FACT-B (OR = 2.18, 95 % CI: 1.72-2.77), physical well-being (WB) (OR = 1.61, 95 % CI: 1.27-2.03), functional WB (OR = 2.08, 95 % CI: 1.65-2.63), social WB (OR = 3.46, 95 % CI: 2.73-4.39), and emotional WB (OR = 1.67, 95 % CI: 1.33-2.11) scores and higher breast cancer symptoms (OR = 1.48, 95 % CI: 1.18-1.87) compared with socially integrated women. Each social network member independently predicted higher QOL. Simultaneous adjustment for social networks and social support partially attenuated associations between social networks and QOL. The strongest mediator and type of social support that was most predictive of QOL outcomes was "positive social interaction." However, each type of support was important depending on outcome, stage, and treatment status. Larger social networks and greater social support were related to higher QOL after a diagnosis of breast cancer. Effective social support interventions need to evolve beyond social-emotional interventions and need to account for disease severity and treatment status.

Enhancing topology adaptation in information-sharing social networks

NASA Astrophysics Data System (ADS)

Cimini, Giulio; Chen, Duanbing; Medo, Matúš; Lü, Linyuan; Zhang, Yi-Cheng; Zhou, Tao

2012-04-01

The advent of the Internet and World Wide Web has led to unprecedent growth of the information available. People usually face the information overload by following a limited number of sources which best fit their interests. It has thus become important to address issues like who gets followed and how to allow people to discover new and better information sources. In this paper we conduct an empirical analysis of different online social networking sites and draw inspiration from its results to present different source selection strategies in an adaptive model for social recommendation. We show that local search rules which enhance the typical topological features of real social communities give rise to network configurations that are globally optimal. These rules create networks which are effective in information diffusion and resemble structures resulting from real social systems.

A cognitive-consistency based model of population wide attitude change.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lakkaraju, Kiran; Speed, Ann Elizabeth

Attitudes play a significant role in determining how individuals process information and behave. In this paper we have developed a new computational model of population wide attitude change that captures the social level: how individuals interact and communicate information, and the cognitive level: how attitudes and concept interact with each other. The model captures the cognitive aspect by representing each individuals as a parallel constraint satisfaction network. The dynamics of this model are explored through a simple attitude change experiment where we vary the social network and distribution of attitudes in a population.

A rumor transmission model with incubation in social networks

NASA Astrophysics Data System (ADS)

Jia, Jianwen; Wu, Wenjiang

2018-02-01

In this paper, we propose a rumor transmission model with incubation period and constant recruitment in social networks. By carrying out an analysis of the model, we study the stability of rumor-free equilibrium and come to the local stable condition of the rumor equilibrium. We use the geometric approach for ordinary differential equations for showing the global stability of the rumor equilibrium. And when ℜ0 = 1, the new model occurs a transcritical bifurcation. Furthermore, numerical simulations are used to support the analysis. At last, some conclusions are presented.

Social network correlates of risky sexual behavior among adolescents in Bahir Dar and Mecha Districts, North West Ethiopia: an institution-based study.

PubMed

Asrese, Kerebih; Mekonnen, Alemtsehay

2018-04-11

Behaviors established during adolescence such as risky sexual behaviors have negative effects on future health and well-being. Extant literature indicated that individual attributes such as peer pressure and substance use have impacts on healthy development of young peoples' sexual behavior. The patterns of relationships (social network structure) and the social network content (members' norm regarding sexual practice) established by adolescents' network on adolescents' risky sexual behaviors are not well investigated. This cross-sectional study assessed the roles of social networks on sexual behavior of high school adolescents in Bahir Dar and Mecha district, North West Ethiopia. Data were collected from 806 high school adolescents using a pretested anonymously self administered questionnaire. Hierarchical logistic regression model was used for analysis. The results indicated that more than 13% had risky sexual behavior. Taking social networks into account improved the explanation of risky sexual behavior over individual attributes. Adolescents embedded within increasing sexual practice approving norm (AOR 1.61; 95%CI: 1.04 - 2.50), increasing network tie strength (AOR 1.12; 95% CI: 1.06 - 1.19), and homogeneous networks (AOR 1.58; 95% CI: .98 - 2.55) were more likely to had risky sexual behavior. Engaging within increasing number of sexuality discussion networks was found protective of risky sexual behavior (AOR .84; 95% CI: .72 - .97). Social networks better predict adolescent's risky sexual behavior than individual attributes. The findings indicated the circumstances or contexts that social networks exert risks or protective effects on adolescents' sexual behavior. Programs designed to reduce school adolescents' sexual risk behavior should consider their patterns of social relationships.

Social Media and Health Education: What the Early Literature Says

ERIC Educational Resources Information Center

Gorham, Robyn; Carter, Lorraine; Nowrouzi, Behdin; McLean, Natalie; Guimond, Melissa

2012-01-01

Social media allows for a wealth of social interactions. More recently, there is a growing use of social media for the purposes of health education. Using an adaptation of the Networked student model by Drexler (2010) as a conceptual model, this article conducts a literature review focusing on the use of social media for health education purposes.…

Rapid social network assessment for predicting HIV and STI risk among men attending bars and clubs in San Diego, California.

PubMed

Drumright, Lydia N; Frost, Simon D W

2010-12-01

To test the use of a rapid assessment tool to determine social network size, and to test whether social networks with a high density of HIV/sexually transmitted infection (STI) or substance using persons were independent predictors of HIV and STI status among men who have sex with men (MSM) using a rapid tool for collecting network information. We interviewed 609 MSM from 14 bars in San Diego, California, USA, using an enhanced version of the Priorities for Local AIDS Control Efforts (PLACE) methodology. Social network size was assessed using a series of 19 questions of the form 'How many people do you know that have the name X?', where X included specific male and female names (eg, Keith), use illicit substances, and have HIV. Generalised linear models were used to estimate average and group-specific network sizes, and their association with HIV status, STI history and methamphetamine use. Despite possible errors in ascertaining network size, average reported network sizes were larger for larger groups. Those who reported having HIV infection or having past STI reported significantly more HIV infected and methamphetamine or popper using individuals in their social network. There was a dose-dependent effect of social network size of HIV infected individuals on self-reported HIV status, past STI and use of methamphetamine in the last 12 months, after controlling for age, ethnicity and numbers of sexual partners in the last year. Relatively simple measures of social networks are associated with HIV/STI risk, and may provide a useful tool for targeting HIV/STI surveillance and prevention.

Characterizing interactions in online social networks during exceptional events

NASA Astrophysics Data System (ADS)

Omodei, Elisa; De Domenico, Manlio; Arenas, Alex

2015-08-01

Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. In this paper, we focus on a specific online social network, Twitter, and we analyze multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered. We consider important events that occurred in different arenas that range from policy to culture or science. For each dataset, the users' online activities are modeled by a multilayer network in which each layer conveys a different kind of interaction, specifically: retweeting, mentioning and replying. This representation allows us to unveil that these distinct types of interaction produce networks with different statistical properties, in particular concerning the degree distribution and the clustering structure. These results suggests that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions. Secondly, our analysis unveils the presence of statistical regularities among the different events, suggesting that the non-trivial topological patterns that we observe may represent universal features of the social dynamics on online social networks during exceptional events.

New approaches to model and study social networks

NASA Astrophysics Data System (ADS)

Lind, P. G.; Herrmann, H. J.

2007-07-01

We describe and develop three recent novelties in network research which are particularly useful for studying social systems. The first one concerns the discovery of some basic dynamical laws that enable the emergence of the fundamental features observed in social networks, namely the nontrivial clustering properties, the existence of positive degree correlations and the subdivision into communities. To reproduce all these features, we describe a simple model of mobile colliding agents, whose collisions define the connections between the agents which are the nodes in the underlying network, and develop some analytical considerations. The second point addresses the particular feature of clustering and its relationship with global network measures, namely with the distribution of the size of cycles in the network. Since in social bipartite networks it is not possible to measure the clustering from standard procedures, we propose an alternative clustering coefficient that can be used to extract an improved normalized cycle distribution in any network. Finally, the third point addresses dynamical processes occurring on networks, namely when studying the propagation of information in them. In particular, we focus on the particular features of gossip propagation which impose some restrictions in the propagation rules. To this end we introduce a quantity, the spread factor, which measures the average maximal fraction of nearest neighbours which get in contact with the gossip, and find the striking result that there is an optimal non-trivial number of friends for which the spread factor is minimized, decreasing the danger of being gossiped about.

Pattern Analysis in Social Networks with Dynamic Connections

NASA Astrophysics Data System (ADS)

Wu, Yu; Zhang, Yu

In this paper, we explore how decentralized local interactions of autonomous agents in a network relate to collective behaviors. Most existing work in this area models social network in which agent relations are fixed; instead, we focus on dynamic social networks where agents can rationally adjust their neighborhoods based on their individual interests. We propose a new connection evaluation rule called the Highest Weighted Reward (HWR) rule, with which agents dynamically choose their neighbors in order to maximize their own utilities based on the rewards from previous interactions. Our experiments show that in the 2-action pure coordination game, our system will stabilize to a clustering state where all relationships in the network are rewarded with the optimal payoff. Our experiments also reveal additional interesting patterns in the network.

Using Social Network Analysis to Investigate Positive EOL Communication.

PubMed

Xu, Jiayun; Yang, Rumei; Wilson, Andrew; Reblin, Maija; Clayton, Margaret F; Ellington, Lee

2018-04-30

End of life (EOL) communication is a complex process involving the whole family and multiple care providers. Applications of analysis techniques that account for communication beyond the patient and patient/provider, will improve clinical understanding of EOL communication. To introduce the use of social network analysis to EOL communication data, and to provide an example of applying social network analysis to home hospice interactions. We provide a description of social network analysis using social network analysis to model communication patterns during home hospice nursing visits. We describe three social network attributes (i.e. magnitude, directionality, and reciprocity) in the expression of positive emotion among hospice nurses, family caregivers, and hospice cancer patients. Differences in communication structure by primary family caregiver gender and across time were also examined. Magnitude (frequency) in the expression of positive emotion occurred most often between nurses and caregivers or nurses and patients. Female caregivers directed more positive emotion to nurses, and nurses directed more positive emotion to other family caregivers when the primary family caregiver was male. Reciprocity (mutuality) in positive emotion declined towards day of death, but increased on day of actual patient death. There was variation in reciprocity by the type of positive emotion expressed. Our example demonstrates that social network analysis can be used to better understand the process of EOL communication. Social network analysis can be expanded to other areas of EOL research, such as EOL decision-making and health care teamwork. Copyright © 2018. Published by Elsevier Inc.

A Study of Malware Propagation via Online Social Networking

NASA Astrophysics Data System (ADS)

Faghani, Mohammad Reza; Nguyen, Uyen Trang

The popularity of online social networks (OSNs) have attracted malware creators who would use OSNs as a platform to propagate automated worms from one user's computer to another's. On the other hand, the topic of malware propagation in OSNs has only been investigated recently. In this chapter, we discuss recent advances on the topic of malware propagation by way of online social networking. In particular, we present three malware propagation techniques in OSNs, namely cross site scripting (XSS), Trojan and clickjacking types, and their characteristics via analytical models and simulations.

Effect of Spatial-Dependent Utility on Social Group Domination

NASA Astrophysics Data System (ADS)

Rodriguez, Nathaniel; Meyertholen, Andrew

2012-02-01

The mathematical modeling of social group competition has garnered much attention. We consider a model originated by Abrams and Strogatz [Nature 424, 900 (2003)] that predicts the extinction of one of two social groups. This model assigns a utility to each social group, which is constant over the entire society. We find by allowing this utility to vary over a society, through the introduction of a network or spatial dependence, this model may result in the coexistence of the two social groups.

Snaps, Selfies, and Shares: How Three Popular Social Media Platforms Contribute to the Sociocultural Model of Disordered Eating Among Young Women.

PubMed

Saunders, Jessica F; Eaton, Asia A

2018-06-01

The current study aimed to integrate and test the sociocultural model of disordered eating with theories explaining the impact of mass media on the development of disordered eating for users of three popular social networking platforms. Young women social networking site (SNS) users (age 18-24) who had never received an eating disorder diagnosis (N = 637) completed questions capturing their SNS gratifications and usage, body surveillance, social comparisons, body dissatisfaction, and eating pathology. Measures were administered in one online session. Model relationships were similar across users of all three SNS platforms: Facebook, Instagram, and Snapchat. Users of all platforms demonstrated a significant positive relationship between upward comparisons and disordered eating outcomes, and between body surveillance and disordered eating outcomes, although differences between models did emerge. Empirical findings support extending the sociocultural model of disordered eating to include SNS uses and gratifications.

Network integration and limits to social inheritance in vervet monkeys.

PubMed

Jarrett, Jonathan D; Bonnell, Tyler R; Young, Christopher; Barrett, Louise; Henzi, S Peter

2018-04-11

Social networks can be adaptive for members and a recent model (Ilany and Akçay 2016 Nat. Comm. 7 , 12084 (doi:10.1038/ncomms12084)) has demonstrated that network structure can be maintained by a simple process of social inheritance. Here, we ask how juvenile vervet monkeys integrate into their adult grooming networks, using the model to test whether observed grooming patterns replicate network structure. Female juveniles, who are philopatric, increased their grooming effort towards adults more than males, although this was not reciprocated by the adults themselves. While more consistent maternal grooming networks, together with maternal network strength, predicted increasing similarity in the patterning of mother-daughter grooming allocations, daughters' grooming networks generally did not match closely those of their mothers. However, maternal networks themselves were not very consistent across time, thus presenting youngsters with a moving target that may be difficult to match. Observed patterns of juvenile female grooming did not replicate the adult network, for which increased association with adults not groomed by their mothers would be necessary. These results suggest that network flexibility, not stability, characterizes our groups and that juveniles are exposed to, and must learn to cope with, temporal shifts in network structure. We hypothesize that this may lead to individual variation in behavioural flexibility, which in turn may help explain why and how variation in sociability influences fitness. © 2018 The Author(s).

a Model for Brand Competition Within a Social Network

NASA Astrophysics Data System (ADS)

Huerta-Quintanilla, R.; Canto-Lugo, E.; Rodríguez-Achach, M.

An agent-based model was built representing an economic environment in which m brands are competing for a product market. These agents represent companies that interact within a social network in which a certain agent persuades others to update or shift their brands; the brands of the products they are using. Decision rules were established that caused each agent to react according to the economic benefits it would receive; they updated/shifted only if it was beneficial. Each agent can have only one of the m possible brands, and she can interact with its two nearest neighbors and another set of agents which are chosen according to a particular set of rules in the network topology. An absorbing state was always reached in which a single brand monopolized the network (known as condensation). The condensation time varied as a function of model parameters is studied including an analysis of brand competition using different networks.

Modeling Dynamic Evolution of Online Friendship Network

NASA Astrophysics Data System (ADS)

Wu, Lian-Ren; Yan, Qiang

2012-10-01

In this paper, we study the dynamic evolution of friendship network in SNS (Social Networking Site). Our analysis suggests that an individual joining a community depends not only on the number of friends he or she has within the community, but also on the friendship network generated by those friends. In addition, we propose a model which is based on two processes: first, connecting nearest neighbors; second, strength driven attachment mechanism. The model reflects two facts: first, in the social network it is a universal phenomenon that two nodes are connected when they have at least one common neighbor; second, new nodes connect more likely to nodes which have larger weights and interactions, a phenomenon called strength driven attachment (also called weight driven attachment). From the simulation results, we find that degree distribution P(k), strength distribution P(s), and degree-strength correlation are all consistent with empirical data.