Introduction to Social Network Analysis

NASA Astrophysics Data System (ADS)

Zaphiris, Panayiotis; Ang, Chee Siang

Social Network analysis focuses on patterns of relations between and among people, organizations, states, etc. It aims to describe networks of relations as fully as possible, identify prominent patterns in such networks, trace the flow of information through them, and discover what effects these relations and networks have on people and organizations. Social network analysis offers a very promising potential for analyzing human-human interactions in online communities (discussion boards, newsgroups, virtual organizations). This Tutorial provides an overview of this analytic technique and demonstrates how it can be used in Human Computer Interaction (HCI) research and practice, focusing especially on Computer Mediated Communication (CMC). This topic acquires particular importance these days, with the increasing popularity of social networking websites (e.g., youtube, myspace, MMORPGs etc.) and the research interest in studying them.

Using Reputation Based Trust to Overcome Malfunctions and Malicious Failures in Electric Power Protection Systems

DTIC Science & Technology

2011-09-01

concert with a physical attack. Additionally, the importance of preventive measures implemented by a social human network to counteract a cyber attack...integrity of the data stored on specific computers. This coordinated cyber attack would have been successful if not for the trusted social network...established by Mr. Hillar Aarelaid, head of the Estonian computer 6 emergency response team (CERT). This social network consisted of Mr. Hillar Aarelaid

A Research on the Use of Social Media Networks by Teacher Candidates

ERIC Educational Resources Information Center

Bolat, Yaviz

2018-01-01

Social media networks are the most important product of the development of computer and communication technologies that affect social life. Social media networks have become a driving force in social and cultural development, while providing social contact for people. This force has improved its sphere of influence over societies in many fields…

Optimal social-networking strategy is a function of socioeconomic conditions.

PubMed

Oishi, Shigehiro; Kesebir, Selin

2012-12-01

In the two studies reported here, we examined the relation among residential mobility, economic conditions, and optimal social-networking strategy. In study 1, a computer simulation showed that regardless of economic conditions, having a broad social network with weak friendship ties is advantageous when friends are likely to move away. By contrast, having a small social network with deep friendship ties is advantageous when the economy is unstable but friends are not likely to move away. In study 2, we examined the validity of the computer simulation using a sample of American adults. Results were consistent with the simulation: American adults living in a zip code where people are residentially stable but economically challenged were happier if they had a narrow but deep social network, whereas in other socioeconomic conditions, people were generally happier if they had a broad but shallow networking strategy. Together, our studies demonstrate that the optimal social-networking strategy varies as a function of socioeconomic conditions.

From biological and social network metaphors to coupled bio-social wireless networks

PubMed Central

Barrett, Christopher L.; Eubank, Stephen; Anil Kumar, V.S.; Marathe, Madhav V.

2010-01-01

Biological and social analogies have been long applied to complex systems. Inspiration has been drawn from biological solutions to solve problems in engineering products and systems, ranging from Velcro to camouflage to robotics to adaptive and learning computing methods. In this paper, we present an overview of recent advances in understanding biological systems as networks and use this understanding to design and analyse wireless communication networks. We expand on two applications, namely cognitive sensing and control and wireless epidemiology. We discuss how our work in these two applications is motivated by biological metaphors. We believe that recent advances in computing and communications coupled with advances in health and social sciences raise the possibility of studying coupled bio-social communication networks. We argue that we can better utilise the advances in our understanding of one class of networks to better our understanding of the other. PMID:21643462

Social network modulation of reward-related signals

PubMed Central

Fareri, Dominic S.; Niznikiewicz, Michael A.; Lee, Victoria K.; Delgado, Mauricio R.

2012-01-01

Everyday goals and experiences are often shared with others who may hold different places within our social networks. We investigated whether the experience of sharing a reward differs with respect to social network. Twenty human participants played a card guessing game for shared monetary outcomes with three partners: a computer, a confederate (out-of-network), and a friend (in-network). Participants subjectively rated the experience of sharing a reward more positively with their friend than the other partners. Neuroimaging results support participants’ subjective reports, as ventral striatal BOLD responses were more robust when sharing monetary gains with a friend, as compared to with the confederate or computer, suggesting a higher value for sharing with an in-network partner. Interestingly, ratings of social closeness co-varied with this activity, resulting in a significant partner × closeness interaction: exploratory analysis showed that only participants reporting higher levels of closeness demonstrated partner-related differences in striatal BOLD response. These results suggest that reward valuation in social contexts is sensitive to distinctions of social network, such that sharing positive experiences with in-network others may carry higher value. PMID:22745503

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.

Optimizing Targeting of Intrusion Detection Systems in Social Networks

NASA Astrophysics Data System (ADS)

Puzis, Rami; Tubi, Meytal; Elovici, Yuval

Internet users communicate with each other in various ways: by Emails, instant messaging, social networking, accessing Web sites, etc. In the course of communicating, users may unintentionally copy files contaminated with computer viruses and worms [1, 2] to their computers and spread them to other users [3]. (Hereafter we will use the term "threats", rather than computer viruses and computer worms). The Internet is the chief source of these threats [4].

Language Views on Social Networking Sites for Language Learning: The Case of Busuu

ERIC Educational Resources Information Center

Álvarez Valencia, José Aldemar

2016-01-01

Social networking has compelled the area of computer-assisted language learning (CALL) to expand its research palette and account for new virtual ecologies that afford language learning and socialization. This study focuses on Busuu, a social networking site for language learning (SNSLL), and analyzes the views of language that are enacted through…

Leveraging Social Networking in the United States Army

DTIC Science & Technology

2011-03-16

ABSTRACT In 2007, the U. S. Department of Defense (DoD) began blocking social networking sites such as including YouTube and MySpace from its computer...issued a memorandum that set a new policy allowing access to social - networking services ( SNS ) from its network . The policy allows all users of...CLASSIFICATION: Unclassified In 2007, the U. S. Department of Defense (DoD) began blocking social networking sites such as including YouTube and MySpace

Effective seeding strategy in evolutionary prisoner's dilemma games on online social networks

NASA Astrophysics Data System (ADS)

Xu, Bo; Shi, Huibin; Wang, Jianwei; Huang, Yun

2015-04-01

This paper explores effective seeding strategies in prisoner's dilemma game (PDG) on online social networks, i.e. the optimal strategy to obtain global cooperation with minimum cost. Three distinct seeding strategies are compared by performing computer simulations on real online social network datasets. Our finding suggests that degree centrality seeding outperforms other strategies regardless of the initial payoff setting or network size. Celebrities of online social networks play key roles in preserving cooperation.

Social Networks, Communication Styles, and Learning Performance in a CSCL Community

ERIC Educational Resources Information Center

Cho, Hichang; Gay, Geri; Davidson, Barry; Ingraffea, Anthony

2007-01-01

The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning…

Leveraging to Win: The Marine Corps Confronts the Intelligence Challenges of Social Media Versus Operations Security

DTIC Science & Technology

2011-01-01

infonnation on the use of social media, . social networking sites were searched to review online forwns. These forums assisted in identifying some of the...tools such as social networking sites , social media, user-generated contentj social softwC\\.fe, email, instant messaging, and discussion forums (e.g...social media on official computers. The ban was put into effect because, as was stated, "[social media and social networking sites ] in general are a

Interactions among human behavior, social networks, and societal infrastructures: A Case Study in Computational Epidemiology

NASA Astrophysics Data System (ADS)

Barrett, Christopher L.; Bisset, Keith; Chen, Jiangzhuo; Eubank, Stephen; Lewis, Bryan; Kumar, V. S. Anil; Marathe, Madhav V.; Mortveit, Henning S.

Human behavior, social networks, and the civil infrastructures are closely intertwined. Understanding their co-evolution is critical for designing public policies and decision support for disaster planning. For example, human behaviors and day to day activities of individuals create dense social interactions that are characteristic of modern urban societies. These dense social networks provide a perfect fabric for fast, uncontrolled disease propagation. Conversely, people’s behavior in response to public policies and their perception of how the crisis is unfolding as a result of disease outbreak can dramatically alter the normally stable social interactions. Effective planning and response strategies must take these complicated interactions into account. In this chapter, we describe a computer simulation based approach to study these issues using public health and computational epidemiology as an illustrative example. We also formulate game-theoretic and stochastic optimization problems that capture many of the problems that we study empirically.

Social Networks Users: Fear of Missing out in Preservice Teachers

ERIC Educational Resources Information Center

Gezgin, Deniz Mertkan; Hamutoglu, Nazire Burcin; Gemikonakli, Orhan; Raman, Ilhan

2017-01-01

As mobile computing and smartphones become an integrated part of our lives, the time individuals spend on social networks has significantly increased. Moreover, a link has been established between the uncontrolled use of social networks to the development of undesirable habits and behaviors including addictions. One such behavior, namely, fear of…

The neural representation of social networks.

PubMed

Weaverdyck, Miriam E; Parkinson, Carolyn

2018-05-24

The computational demands associated with navigating large, complexly bonded social groups are thought to have significantly shaped human brain evolution. Yet, research on social network representation and cognitive neuroscience have progressed largely independently. Thus, little is known about how the human brain encodes the structure of the social networks in which it is embedded. This review highlights recent work seeking to bridge this gap in understanding. While the majority of research linking social network analysis and neuroimaging has focused on relating neuroanatomy to social network size, researchers have begun to define the neural architecture that encodes social network structure, cognitive and behavioral consequences of encoding this information, and individual differences in how people represent the structure of their social world. Copyright © 2018 Elsevier Ltd. All rights reserved.

Social network extraction based on Web: 3. the integrated superficial method

NASA Astrophysics Data System (ADS)

Nasution, M. K. M.; Sitompul, O. S.; Noah, S. A.

2018-03-01

The Web as a source of information has become part of the social behavior information. Although, by involving only the limitation of information disclosed by search engines in the form of: hit counts, snippets, and URL addresses of web pages, the integrated extraction method produces a social network not only trusted but enriched. Unintegrated extraction methods may produce social networks without explanation, resulting in poor supplemental information, or resulting in a social network of durmise laden, consequently unrepresentative social structures. The integrated superficial method in addition to generating the core social network, also generates an expanded network so as to reach the scope of relation clues, or number of edges computationally almost similar to n(n - 1)/2 for n social actors.

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…

Assessing Group Interaction with Social Language Network Analysis

NASA Astrophysics Data System (ADS)

Scholand, Andrew J.; Tausczik, Yla R.; Pennebaker, James W.

In this paper we discuss a new methodology, social language network analysis (SLNA), that combines tools from social language processing and network analysis to assess socially situated working relationships within a group. Specifically, SLNA aims to identify and characterize the nature of working relationships by processing artifacts generated with computer-mediated communication systems, such as instant message texts or emails. Because social language processing is able to identify psychological, social, and emotional processes that individuals are not able to fully mask, social language network analysis can clarify and highlight complex interdependencies between group members, even when these relationships are latent or unrecognized.

77 FR 72335 - Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-05

... computer networks, systems, or databases. The records contain the individual's name; social security number... control and track access to DLA-controlled networks, computer systems, and databases. The records may also...

Sense-making for intelligence analysis on social media data

NASA Astrophysics Data System (ADS)

Pritzkau, Albert

2016-05-01

Social networks, in particular online social networks as a subset, enable the analysis of social relationships which are represented by interaction, collaboration, or other sorts of influence between people. Any set of people and their internal social relationships can be modelled as a general social graph. These relationships are formed by exchanging emails, making phone calls, or carrying out a range of other activities that build up the network. This paper presents an overview of current approaches to utilizing social media as a ubiquitous sensor network in the context of national and global security. Exploitation of social media is usually an interdisciplinary endeavour, in which the relevant technologies and methods are identified and linked in order ultimately demonstrate selected applications. Effective and efficient intelligence is usually accomplished in a combined human and computer effort. Indeed, the intelligence process heavily depends on combining a human's flexibility, creativity, and cognitive ability with the bandwidth and processing power of today's computers. To improve the usability and accuracy of the intelligence analysis we will have to rely on data-processing tools at the level of natural language. Especially the collection and transformation of unstructured data into actionable, structured data requires scalable computational algorithms ranging from Artificial Intelligence, via Machine Learning, to Natural Language Processing (NLP). To support intelligence analysis on social media data, social media analytics is concerned with developing and evaluating computational tools and frameworks to collect, monitor, analyze, summarize, and visualize social media data. Analytics methods are employed to extract of significant patterns that might not be obvious. As a result, different data representations rendering distinct aspects of content and interactions serve as a means to adapt the focus of the intelligence analysis to specific information requests.

Computers, Networks, and Desegregation at San Jose High Academy.

ERIC Educational Resources Information Center

Solomon, Gwen

1987-01-01

Describes magnet high school which was created in California to meet desegregation requirements and emphasizes computer technology. Highlights include local computer networks that connect science and music labs, the library/media center, business computer lab, writing lab, language arts skills lab, and social studies classrooms; software; teacher…

Email networks and the spread of computer viruses

NASA Astrophysics Data System (ADS)

Newman, M. E.; Forrest, Stephanie; Balthrop, Justin

2002-09-01

Many computer viruses spread via electronic mail, making use of computer users' email address books as a source for email addresses of new victims. These address books form a directed social network of connections between individuals over which the virus spreads. Here we investigate empirically the structure of this network using data drawn from a large computer installation, and discuss the implications of this structure for the understanding and prevention of computer virus epidemics.

Using Social Network Graphs as Visualization Tools to Influence Peer Selection Decision-Making Strategies to Access Information about Complex Socioscientific Issues

ERIC Educational Resources Information Center

Yoon, Susan A.

2011-01-01

This study extends previous research that explores how visualization affordances that computational tools provide and social network analyses that account for individual- and group-level dynamic processes can work in conjunction to improve learning outcomes. The study's main hypothesis is that when social network graphs are used in instruction,…

Point-Process Models of Social Network Interactions: Parameter Estimation and Missing Data Recovery

DTIC Science & Technology

2014-08-01

treating them as zero will have a de minimis impact on the results, but avoiding computing them (and computing with them) saves tremendous time. Set a... test the methods on simulated time series on artificial social networks, including some toy networks and some meant to resemble IkeNet. We conclude...the section by discussing the results in detail. In each of our tests we begin with a complete data set, whether it is real (IkeNet) or simulated. Then

A computer-assisted motivational social network intervention to reduce alcohol, drug and HIV risk behaviors among Housing First residents.

PubMed

Kennedy, David P; Hunter, Sarah B; Chan Osilla, Karen; Maksabedian, Ervant; Golinelli, Daniela; Tucker, Joan S

2016-03-15

Individuals transitioning from homelessness to housing face challenges to reducing alcohol, drug and HIV risk behaviors. To aid in this transition, this study developed and will test a computer-assisted intervention that delivers personalized social network feedback by an intervention facilitator trained in motivational interviewing (MI). The intervention goal is to enhance motivation to reduce high risk alcohol and other drug (AOD) use and reduce HIV risk behaviors. In this Stage 1b pilot trial, 60 individuals that are transitioning from homelessness to housing will be randomly assigned to the intervention or control condition. The intervention condition consists of four biweekly social network sessions conducted using MI. AOD use and HIV risk behaviors will be monitored prior to and immediately following the intervention and compared to control participants' behaviors to explore whether the intervention was associated with any systematic changes in AOD use or HIV risk behaviors. Social network health interventions are an innovative approach for reducing future AOD use and HIV risk problems, but little is known about their feasibility, acceptability, and efficacy. The current study develops and pilot-tests a computer-assisted intervention that incorporates social network visualizations and MI techniques to reduce high risk AOD use and HIV behaviors among the formerly homeless. CLINICALTRIALS. NCT02140359.

Social Networks Impact Factor on Students' Achievements and Attitudes towards the "Computer in Teaching" Course at the College of Education

ERIC Educational Resources Information Center

Elfeky, Abdellah

2017-01-01

The study aims to examine the impact of social networks of a "Computer in Teaching" course on the achievement and attitudes students at the faculty of education at Najran University. The sample consists of (60) students from the third level in the special education program, (30) students represented the control group whereas the other…

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

Discussion on the Technology and Method of Computer Network Security Management

NASA Astrophysics Data System (ADS)

Zhou, Jianlei

2017-09-01

With the rapid development of information technology, the application of computer network technology has penetrated all aspects of society, changed people's way of life work to a certain extent, brought great convenience to people. But computer network technology is not a panacea, it can promote the function of social development, but also can cause damage to the community and the country. Due to computer network’ openness, easiness of sharing and other characteristics, it had a very negative impact on the computer network security, especially the loopholes in the technical aspects can cause damage on the network information. Based on this, this paper will do a brief analysis on the computer network security management problems and security measures.

Using the D-Wave 2X Quantum Computer to Explore the Formation of Global Terrorist Networks

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

Ambrosiano, John Joseph; Roberts, Randy Mark; Sims, Benjamin Hayden

Social networks with signed edges (+/-) play an important role in an area of social network theory called structural balance. In these networks, edges represent relationships that are labeled as either friendly (+) or hostile (-). A signed social network is balanced only if all cycles of three or more nodes in the graph have an odd number of hostile edges. A fundamental property of a balanced network is that it can be cleanly divided into 2 factions, where all relationships within each faction are friendly, and all relationships between members of different factions are hostile. The more unbalanced amore » network is, the more edges will fail to adhere to this rule, making factions more ambiguous. Social theory suggests unbalanced networks should be unstable, a finding that has been supported by research on gangs, which shows that unbalanced relationships are associated with greater violence, possibly due to this increased ambiguity about factional allegiances (Nakamura et al). One way to estimate the imbalance in a network, if only edge relationships are known, is to assign nodes to factions that minimize the number of violations of the edge rule described above. This problem is known to be computationally NP-hard. However, Facchetti et al. have pointed out that it is equivalent to an Ising model with a Hamiltonian that effectively counts the number of edge rule violations. Therefore, finding the assignment of factions that minimizes energy of the equivalent Ising system yields an estimate of the imbalance in the network. Based on the Ising model equivalence of the signed-social network balance problem, we have used the D-Wave 2X quantum annealing computer to explore some aspects of signed social networks. Because connectivity in the D-Wave computer is limited to its particular native topology, arbitrary networks cannot be represented directly. Rather, they must be “embedded” using a technique in which multiple qubits are chained together with special weights to simulate a collection of nodes with the required connectivity. This limits the size of a fully connected network in the D-Wave to about 50 simulated nodes, using all of the approximately 1150 qubits in the machine. In order to keep within this limitation, while exploring a problem of potential social relevance, we constructed time series of historical network snapshots from Stanford’s Mapping Militants Project, where nodes represent militant organizations, and edges represent either alliances or rivalries between organizations. We constructed two series from different theaters – Iraq and Syria – spanning timelines from about 2000 to 2016, each with networks whose maximum size was in the 20-30 node range. Computationally, our experience suggests D-Wave technology is promising, providing fast, nearly constant scaling of computational effort in the main part of the calculation that relies on the quantum annealing cycle. However, the cost of embedding an arbitrary network of interest in the D-Wave native topology scales poorly. If the embedding cost can be amortized relative to the annealing cycle, it may be possible to gain a substantial advantage over classical computing methods, provided a large enough network can be accommodated by partitioning into subnetworks or some similar strategy. In terms of our application to networks of militant organizations, we found a rise in network imbalance in the Syrian theater that appears to correspond roughly with the entrance of the Islamic State into a milieu already populated with other groups, a phenomenon we plan to explore in more detail. In these very preliminary results, we also noticed that during at least one period where both the size and imbalance of the network increased substantially, the imbalance per edge seemed to remain fairly steady. This may suggest some adaptive behavior among the participating factions, which may also warrant further exploration.« less

Universal Batch Steganalysis

DTIC Science & Technology

2014-06-30

steganalysis) in large-scale datasets such as might be obtained by monitoring a corporate network or social network. Identifying guilty actors...guilty’ user (of steganalysis) in large-scale datasets such as might be obtained by monitoring a corporate network or social network. Identifying guilty...floating point operations (1 TFLOPs) for a 1 megapixel image. We designed a new implementation using Compute Unified Device Architecture (CUDA) on NVIDIA

Optimization Techniques for Analysis of Biological and Social Networks

DTIC Science & Technology

2012-03-28

analyzing a new metaheuristic technique, variable objective search. 3. Experimentation and application: Implement the proposed algorithms , test and fine...alternative mathematical programming formulations, their theoretical analysis, the development of exact algorithms , and heuristics. Originally, clusters...systematic fashion under a unifying theoretical and algorithmic framework. Optimization, Complex Networks, Social Network Analysis, Computational

The role of social media for patients and consumer health. Contribution of the IMIA Consumer Health Informatics Working Group.

PubMed

Lau, A Y S; Siek, K A; Fernandez-Luque, L; Tange, H; Chhanabhai, P; Li, S Y W; Elkin, P L; Arjabi, A; Walczowski, L; Ang, C S; Eysenbach, G

2011-01-01

: To provide an overview on social media for consumers and patients in areas of health behaviours and outcomes. A directed review of recent literature. : We discuss the limitations and challenges of social media, ranging from social network sites (SNSs), computer games, mobile applications, to online videos. An overview of current users of social media (Generation Y), and potential users (such as low socioeconomic status and the chronically ill populations) is also presented. Future directions in social media research are also discussed. : We encourage the health informatics community to consider the socioeconomic class, age, culture, and literacy level of their populations, and select an appropriate medium and platform when designing social networked interventions for health. Little is known about the impact of second-hand experiences faciliated by social media, nor the quality and safety of social networks on health. Methodologies and theories from human computer interaction, human factors engineering and psychology may help guide the challenges in designing and evaluating social networked interventions for health. Further, by analysing how people search and navigate social media for health purposes, infodemiology and infoveillance are promising areas of research that should provide valuable insights on present and emergening health behaviours on a population scale.

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.

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…

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.

Why Do Computer Viruses Survive In The Internet?

NASA Astrophysics Data System (ADS)

Ifti, Margarita; Neumann, Paul

2010-01-01

We use the three-species cyclic competition model (Rock-Paper-Scissors), described by reactions A+B→2B, B+C→2C, C+A→2A, for emulating a computer network with e-mail viruses. Different topologies of the network bring about different dynamics of the epidemics. When the parameters of the network are varied, it is observed that very high clustering coefficients are necessary for a pandemics to happen. The differences between the networks of computer users, e-mail networks, and social networks, as well as their role in determining the nature of epidemics are also discussed.

Networking Micro-Processors for Effective Computer Utilization in Nursing

PubMed Central

Mangaroo, Jewellean; Smith, Bob; Glasser, Jay; Littell, Arthur; Saba, Virginia

1982-01-01

Networking as a social entity has important implications for maximizing computer resources for improved utilization in nursing. This paper describes the one process of networking of complementary resources at three institutions. Prairie View A&M University, Texas A&M University and the University of Texas School of Public Health, which has effected greater utilization of computers at the college. The results achieved in this project should have implications for nurses, users, and consumers in the development of computer resources.

Forensic analysis of social networking application on iOS devices

NASA Astrophysics Data System (ADS)

Zhang, Shuhui; Wang, Lianhai

2013-12-01

The increased use of social networking application on iPhone and iPad make these devices a goldmine for forensic investigators. Besides, QQ, Wechat, Sina Weibo and skype applications are very popular in China and didn't draw attention to researchers. These social networking applications are used not only on computers, but also mobile phones and tablets. This paper focuses on conducting forensic analysis on these four social networking applications on iPhone and iPad devices. The tests consisted of installing the social networking applications on each device, conducting common user activities through each application and correlation analysis with other activities. Advices to the forensic investigators are also given. It could help the investigators to describe the crime behavior and reconstruct the crime venue.

Computer Mediated Communication for Social and Academic Purposes: Profiles of Use and University Students' Gratifications

ERIC Educational Resources Information Center

Vrocharidou, Anatoli; Efthymiou, Ilias

2012-01-01

The present study approaches the Internet as a social space, where university students make use of computer mediated communication (CMC) applications, i.e. e-mail, instant messaging and social network sites, in order to satisfy social and academic needs. We focus on university students, because they represent one of the most avid groups of CMC…

Chinese EFL Teachers' Social Interaction and Socio-Cognitive Presence in Synchronous Computer-Mediated Communication

ERIC Educational Resources Information Center

Wu, Heping; Gao, Junde; Zhang, Weimin

2014-01-01

The present study examines the professional growth of three Chinese English teachers by analyzing their interactional patterns and their social and cognitive presence in an online community. The data from social network analysis (SNA) and content analysis revealed that computer-mediated communication (CMC) created new opportunities for teachers to…

Ranking network of a captive rhesus macaque society: a sophisticated corporative kingdom.

PubMed

Fushing, Hsieh; McAssey, Michael P; Beisner, Brianne; McCowan, Brenda

2011-03-15

We develop a three-step computing approach to explore a hierarchical ranking network for a society of captive rhesus macaques. The computed network is sufficiently informative to address the question: Is the ranking network for a rhesus macaque society more like a kingdom or a corporation? Our computations are based on a three-step approach. These steps are devised to deal with the tremendous challenges stemming from the transitivity of dominance as a necessary constraint on the ranking relations among all individual macaques, and the very high sampling heterogeneity in the behavioral conflict data. The first step simultaneously infers the ranking potentials among all network members, which requires accommodation of heterogeneous measurement error inherent in behavioral data. Our second step estimates the social rank for all individuals by minimizing the network-wide errors in the ranking potentials. The third step provides a way to compute confidence bounds for selected empirical features in the social ranking. We apply this approach to two sets of conflict data pertaining to two captive societies of adult rhesus macaques. The resultant ranking network for each society is found to be a sophisticated mixture of both a kingdom and a corporation. Also, for validation purposes, we reanalyze conflict data from twenty longhorn sheep and demonstrate that our three-step approach is capable of correctly computing a ranking network by eliminating all ranking error.

Analyzing the Dynamics of Communication in Online Social Networks

NASA Astrophysics Data System (ADS)

de Choudhury, Munmun; Sundaram, Hari; John, Ajita; Seligmann, Doree Duncan

This chapter deals with the analysis of interpersonal communication dynamics in online social networks and social media. Communication is central to the evolution of social systems. Today, the different online social sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements (i.e., image, video) as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information or concepts, and how the media channels impact our online interactional behavior. Our central hypothesis is that such communication dynamics between individuals manifest themselves via two key aspects: the information or concept that is the content of communication, and the channel i.e., the media via which communication takes place. We present computational models and discuss large-scale quantitative observational studies for both these organizing ideas. First, we develop a computational framework to determine the "interestingness" property of conversations cented around rich media. Second, we present user models of diffusion of social actions and study the impact of homophily on the diffusion process. The outcome of this research is twofold. First, extensive empirical studies on datasets from YouTube have indicated that on rich media sites, the conversations that are deemed "interesting" appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Second, observational and computational studies on large social media datasets such as Twitter have indicated that diffusion of social actions in a network can be indicative of future information cascades. Besides, given a topic, these cascades are often a function of attribute homophily existent among the participants. We believe that this chapter can make significant contribution into a better understanding of how we communicate online and how it is redefining our collective sociological behavior.

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.

A spread willingness computing-based information dissemination model.

PubMed

Huang, Haojing; Cui, Zhiming; Zhang, Shukui

2014-01-01

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network.

A Spread Willingness Computing-Based Information Dissemination Model

PubMed Central

Cui, Zhiming; Zhang, Shukui

2014-01-01

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network. PMID:25110738

Prototype of a Mobile Social Network for Education Using Dynamic Web Service

ERIC Educational Resources Information Center

Hoentsch, Sandra Costa Pinto; Carvalho, Felipe Oliveira; Santos, Luiz Marcus Monteiro de Almeida; Ribeiro, Admilson de Ribamar Lima

2012-01-01

This article presents the proposal of a social network site SocialNetLab that belongs to the Department of Computing-Federal University of Sergipe and which aims to locate and notify users of a nearby friend independently of the location technology available in the equipment through dynamic Web Service; to serve as a laboratory for research in…

Linguistic Politeness and Interpersonal Ties among Bengalis on the Social Network Site Orkut[R]: The Bulge Theory Revisited

ERIC Educational Resources Information Center

Das, Anupam

2010-01-01

This study examined linguistic politeness behaviors and their relationship to social distance among members of a diasporic Bengali community on the social network site "Orkut"[R]. Using data from computer-mediated communication (CMC), specifically text messages posted on "Orkut"[R] "scrapbooks," it developed a method to test the claims of the…

Offline Social Relationships and Online Cancer Communication: Effects of Social and Family Support on Online Social Network Building.

PubMed

Namkoong, Kang; Shah, Dhavan V; Gustafson, David H

2017-11-01

This study investigates how social support and family relationship perceptions influence breast cancer patients' online communication networks in a computer-mediated social support (CMSS) group. To examine social interactions in the CMSS group, we identified two types of online social networks: open and targeted communication networks. The open communication network reflects group communication behaviors (i.e., one-to-many or "broadcast" communication) in which the intended audience is not specified; in contrast, the targeted communication network reflects interpersonal discourses (i.e., one-to-one or directed communication) in which the audience for the message is specified. The communication networks were constructed by tracking CMSS group usage data of 237 breast cancer patients who participated in one of two National Cancer Institute-funded randomized clinical trials. Eligible subjects were within 2 months of a diagnosis of primary breast cancer or recurrence at the time of recruitment. Findings reveal that breast cancer patients who perceived less availability of offline social support had a larger social network size in the open communication network. In contrast, those who perceived less family cohesion had a larger targeted communication network in the CMSS group, meaning they were inclined to use the CMSS group for developing interpersonal relationships.

Interaction Patterns of Nurturant Support Exchanged in Online Health Social Networking

PubMed Central

Yang, Christopher C

2012-01-01

Background Expressing emotion in online support communities is an important aspect of enabling e-patients to connect with each other and expand their social resources. Indirectly it increases the amount of support for coping with health issues. Exploring the supportive interaction patterns in online health social networking would help us better understand how technology features impacts user behavior in this context. Objective To build on previous research that identified different types of social support in online support communities by delving into patterns of supportive behavior across multiple computer-mediated communication formats. Each format combines different architectural elements, affecting the resulting social spaces. Our research question compared communication across different formats of text-based computer-mediated communication provided on the MedHelp.org health social networking environment. Methods We identified messages with nurturant support (emotional, esteem, and network) across three different computer-mediated communication formats (forums, journals, and notes) of an online support community for alcoholism using content analysis. Our sample consisted of 493 forum messages, 423 journal messages, and 1180 notes. Results Nurturant support types occurred frequently among messages offering support (forum comments: 276/412 messages, 67.0%; journal posts: 65/88 messages, 74%; journal comments: 275/335 messages, 82.1%; and notes: 1002/1180 messages, 84.92%), but less often among messages requesting support. Of all the nurturing supports, emotional (ie, encouragement) appeared most frequently, with network and esteem support appearing in patterns of varying combinations. Members of the Alcoholism Community appeared to adapt some traditional face-to-face forms of support to their needs in becoming sober, such as provision of encouragement, understanding, and empathy to one another. Conclusions The computer-mediated communication format may have the greatest influence on the supportive interactions because of characteristics such as audience reach and access. Other factors include perception of community versus personal space or purpose of communication. These results lead to a need for further research. PMID:22555303

Interaction patterns of nurturant support exchanged in online health social networking.

PubMed

Chuang, Katherine Y; Yang, Christopher C

2012-05-03

Expressing emotion in online support communities is an important aspect of enabling e-patients to connect with each other and expand their social resources. Indirectly it increases the amount of support for coping with health issues. Exploring the supportive interaction patterns in online health social networking would help us better understand how technology features impacts user behavior in this context. To build on previous research that identified different types of social support in online support communities by delving into patterns of supportive behavior across multiple computer-mediated communication formats. Each format combines different architectural elements, affecting the resulting social spaces. Our research question compared communication across different formats of text-based computer-mediated communication provided on the MedHelp.org health social networking environment. We identified messages with nurturant support (emotional, esteem, and network) across three different computer-mediated communication formats (forums, journals, and notes) of an online support community for alcoholism using content analysis. Our sample consisted of 493 forum messages, 423 journal messages, and 1180 notes. Nurturant support types occurred frequently among messages offering support (forum comments: 276/412 messages, 67.0%; journal posts: 65/88 messages, 74%; journal comments: 275/335 messages, 82.1%; and notes: 1002/1180 messages, 84.92%), but less often among messages requesting support. Of all the nurturing supports, emotional (ie, encouragement) appeared most frequently, with network and esteem support appearing in patterns of varying combinations. Members of the Alcoholism Community appeared to adapt some traditional face-to-face forms of support to their needs in becoming sober, such as provision of encouragement, understanding, and empathy to one another. The computer-mediated communication format may have the greatest influence on the supportive interactions because of characteristics such as audience reach and access. Other factors include perception of community versus personal space or purpose of communication. These results lead to a need for further research.

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.

On Learning Cluster Coefficient of Private Networks

PubMed Central

Wang, Yue; Wu, Xintao; Zhu, Jun; Xiang, Yang

2013-01-01

Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as clustering coefficient or modularity often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data. In this paper, we treat a graph statistics as a function f and develop a divide and conquer approach to enforce differential privacy. The basic procedure of this approach is to first decompose the target computation f into several less complex unit computations f1, …, fm connected by basic mathematical operations (e.g., addition, subtraction, multiplication, division), then perturb the output of each fi with Laplace noise derived from its own sensitivity value and the distributed privacy threshold εi, and finally combine those perturbed fi as the perturbed output of computation f. We examine how various operations affect the accuracy of complex computations. When unit computations have large global sensitivity values, we enforce the differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We illustrate our approach by using clustering coefficient, which is a popular statistics used in social network analysis. Empirical evaluations on five real social networks and various synthetic graphs generated from three random graph models show the developed divide and conquer approach outperforms the direct approach. PMID:24429843

Sociospace: A smart social framework based on the IP Multimedia Subsystem

NASA Astrophysics Data System (ADS)

Hasswa, Ahmed

Advances in smart technologies, wireless networking, and increased interest in contextual services have led to the emergence of ubiquitous and pervasive computing as one of the most promising areas of computing in recent years. Smart Spaces, in particular, have gained significant interest within the research community. Currently, most Smart Spaces rely on physical components, such as sensors, to acquire information about the real-world environment. Although current sensor networks can acquire some useful contextual information from the physical environment, their information resources are often limited, and the data acquired is often unreliable. We argue that by introducing social network information into such systems, smarter and more adaptive spaces can be created. Social networks have recently become extremely popular, and are now an integral part of millions of people's daily lives. Through social networks, users create profiles, build relationships, and join groups, forming intermingled sets and communities. Social Networks contain a wealth of information, which, if exploited properly, can lead to a whole new level of smart contextual services. A mechanism is therefore needed to extract data from heterogeneous social networks, to link profiles across different networks, and to aggregate the data obtained. We therefore propose the design and implementation of a Smart Spaces framework that utilizes the social context. In order to manage services and sessions, we integrate our system with the IP Multimedia Subsystem. Our system, which we call SocioSpace, includes full design and implementation of all components, including the central server, the location management system, the social network interfacing system, the service delivery platform, and user agents. We have built a prototype for proof of concept and carried out exhaustive performance analysis; the results show that SocioSpace is scalable, extensible, and fault-tolerant. It is capable of creating Smart Spaces that can truly deliver adaptive services that enhance the users' overall experience, increase their satisfaction, and make the surroundings more beneficial and interesting to them.

Cloud computing.

PubMed

Wink, Diane M

2012-01-01

In this bimonthly series, the author examines how nurse educators can use Internet and Web-based technologies such as search, communication, and collaborative writing tools; social networking and social bookmarking sites; virtual worlds; and Web-based teaching and learning programs. This article describes how cloud computing can be used in nursing education.

Social network extraction based on Web: 1. Related superficial methods

NASA Astrophysics Data System (ADS)

Khairuddin Matyuso Nasution, Mahyuddin

2018-01-01

Often the nature of something affects methods to resolve the related issues about it. Likewise, methods to extract social networks from the Web, but involve the structured data types differently. This paper reveals several methods of social network extraction from the same sources that is Web: the basic superficial method, the underlying superficial method, the description superficial method, and the related superficial methods. In complexity we derive the inequalities between methods and so are their computations. In this case, we find that different results from the same tools make the difference from the more complex to the simpler: Extraction of social network by involving co-occurrence is more complex than using occurrences.

Diversity of social ties in scientific collaboration networks

NASA Astrophysics Data System (ADS)

Shi, Quan; Xu, Bo; Xu, Xiaomin; Xiao, Yanghua; Wang, Wei; Wang, Hengshan

2011-11-01

Diversity is one of the important perspectives to characterize behaviors of individuals in social networks. It is intuitively believed that diversity of social ties accounts for competition advantage and idea innovation. However, quantitative evidences in a real large social network can be rarely found in the previous research. Thanks to the availability of scientific publication records on WWW; now we can construct a large scientific collaboration network, which provides us a chance to gain insight into the diversity of relationships in a real social network through statistical analysis. In this article, we dedicate our efforts to perform empirical analysis on a scientific collaboration network extracted from DBLP, an online bibliographic database in computer science, in a systematical way, finding the following: distributions of diversity indices tend to decay in an exponential or Gaussian way; diversity indices are not trivially correlated to existing vertex importance measures; authors of diverse social ties tend to connect to each other and these authors are generally more competitive than others.

Information Poverty and the Latino Community: Implications for Social Work Practice and Social Work Education.

ERIC Educational Resources Information Center

McNutt, John G.; Queiro-Tajalli, Irene; Boland, Katherine M.; Campbell, Craig

2001-01-01

Education level, computer ownership, and technology and information access determine one's status in the new information economy. Interventions such as community computer networks, telecommuting centers, grassroots electronic commerce, and volunteer technology corps can help Latinos and other marginalized groups overcome continued disadvantage.…

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.

Building Real World Domain-Specific Social Network Websites as a Capstone Project

ERIC Educational Resources Information Center

Yue, Kwok-Bun; De Silva, Dilhar; Kim, Dan; Aktepe, Mirac; Nagle, Stewart; Boerger, Chris; Jain, Anubha; Verma, Sunny

2009-01-01

This paper describes our experience of using Content Management Software (CMS), specifically Joomla, to build a real world domain-specific social network site (SNS) as a capstone project for graduate information systems and computer science students. As Web 2.0 technologies become increasingly important in driving business application development,…

Collaboration Levels in Asynchronous Discussion Forums: A Social Network Analysis Approach

ERIC Educational Resources Information Center

Luhrs, Cecilia; McAnally-Salas, Lewis

2016-01-01

Computer Supported Collaborative Learning literature relates high levels of collaboration to enhanced learning outcomes. However, an agreement on what is considered a high level of collaboration is unclear, especially if a qualitative approach is taken. This study describes how methods of Social Network Analysis were used to design a collaboration…

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 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.

Perceptions of the roles of social networking in simulation augmented medical education and training.

PubMed

Martin, Rob; Rojas, David; Cheung, Jeffrey J H; Weber, Bryce; Kapralos, Bill; Dubrowski, Adam

2013-01-01

Simulation-augmented education and training (SAET) is an expensive educational tool that may be facilitated through social networking technologies or Computer Supported Collaborative Learning (CSCL). This study examined the perceptions of medical undergraduates participating in SAET for knot tying skills to identify perceptions and barriers to implementation of social networking technologies within a broader medical education curriculum. The majority of participants (89%) found CSCL aided their learning of the technical skill and identified privacy and accessibility as major barriers to the tools implementation.

Computers as Media for Communication: Learning and Development in a Whole Earth Context.

ERIC Educational Resources Information Center

Levin, James A.

Educationally successful electronic network activities involving microcomputers and long-distance networks include a student newswire, joint social science projects, and joint science projects. A newswire activity, such as "The Computer Chronicles," can provide a wide range of audiences for writing, a functional environment for reading, and a…

Augmenting Trust Establishment in Dynamic Systems with Social Networks

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

Lagesse, Brent J; Kumar, Mohan; Venkatesh, Svetha

2010-01-01

Social networking has recently flourished in popularity through the use of social websites. Pervasive computing resources have allowed people stay well-connected to each other through access to social networking resources. We take the position that utilizing information produced by relationships within social networks can assist in the establishment of trust for other pervasive computing applications. Furthermore, we describe how such a system can augment a sensor infrastructure used for event observation with information from mobile sensors (ie, mobile phones with cameras) controlled by potentially untrusted third parties. Pervasive computing systems are invisible systems, oriented around the user. As a result,more » many future pervasive systems are likely to include a social aspect to the system. The social communities that are developed in these systems can augment existing trust mechanisms with information about pre-trusted entities or entities to initially consider when beginning to establish trust. An example of such a system is the Collaborative Virtual Observation (CoVO) system fuses sensor information from disaparate sources in soft real-time to recreate a scene that provides observation of an event that has recently transpired. To accomplish this, CoVO must efficently access services whilst protecting the data from corruption from unknown remote nodes. CoVO combines dynamic service composition with virtual observation to utilize existing infrastructure with third party services available in the environment. Since these services are not under the control of the system, they may be unreliable or malicious. When an event of interest occurs, the given infrastructure (bus cameras, etc.) may not sufficiently cover the necessary information (be it in space, time, or sensor type). To enhance observation of the event, infrastructure is augmented with information from sensors in the environment that the infrastructure does not control. These sensors may be unreliable, uncooperative, or even malicious. Additionally, to execute queries in soft real-time, processing must be distributed to available systems in the environment. We propose to use information from social networks to satisfy these requirements. In this paper, we present our position that knowledge gained from social activities can be used to augment trust mechanisms in pervasive computing. The system uses social behavior of nodes to predict a subset that it wants to query for information. In this context, social behavior such as transit patterns and schedules (which can be used to determine if a queried node is likely to be reliable) or known relationships, such as a phone's address book, that can be used to determine networks of nodes that may also be able to assist in retrieving information. Neither implicit nor explicit relationships necessarily imply that the user trusts an entity, but rather will provide a starting place for establishing trust. The proposed framework utilizes social network information to assist in trust establishment when third-party sensors are used for sensing events.« less

The Impacts of Attitudes and Engagement on Electronic Word of Mouth (eWOM) of Mobile Sensor Computing Applications.

PubMed

Zhao, Yu; Liu, Yide; Lai, Ivan K W; Zhang, Hongfeng; Zhang, Yi

2016-03-18

As one of the latest revolutions in networking technology, social networks allow users to keep connected and exchange information. Driven by the rapid wireless technology development and diffusion of mobile devices, social networks experienced a tremendous change based on mobile sensor computing. More and more mobile sensor network applications have appeared with the emergence of a huge amount of users. Therefore, an in-depth discussion on the human-computer interaction (HCI) issues of mobile sensor computing is required. The target of this study is to extend the discussions on HCI by examining the relationships of users' compound attitudes (i.e., affective attitudes, cognitive attitude), engagement and electronic word of mouth (eWOM) behaviors in the context of mobile sensor computing. A conceptual model is developed, based on which, 313 valid questionnaires are collected. The research discusses the level of impact on the eWOM of mobile sensor computing by considering user-technology issues, including the compound attitude and engagement, which can bring valuable discussions on the HCI of mobile sensor computing in further study. Besides, we find that user engagement plays a mediating role between the user's compound attitudes and eWOM. The research result can also help the mobile sensor computing industry to develop effective strategies and build strong consumer user-product (brand) relationships.

The Impacts of Attitudes and Engagement on Electronic Word of Mouth (eWOM) of Mobile Sensor Computing Applications

PubMed Central

Zhao, Yu; Liu, Yide; Lai, Ivan K. W.; Zhang, Hongfeng; Zhang, Yi

2016-01-01

As one of the latest revolutions in networking technology, social networks allow users to keep connected and exchange information. Driven by the rapid wireless technology development and diffusion of mobile devices, social networks experienced a tremendous change based on mobile sensor computing. More and more mobile sensor network applications have appeared with the emergence of a huge amount of users. Therefore, an in-depth discussion on the human–computer interaction (HCI) issues of mobile sensor computing is required. The target of this study is to extend the discussions on HCI by examining the relationships of users’ compound attitudes (i.e., affective attitudes, cognitive attitude), engagement and electronic word of mouth (eWOM) behaviors in the context of mobile sensor computing. A conceptual model is developed, based on which, 313 valid questionnaires are collected. The research discusses the level of impact on the eWOM of mobile sensor computing by considering user-technology issues, including the compound attitude and engagement, which can bring valuable discussions on the HCI of mobile sensor computing in further study. Besides, we find that user engagement plays a mediating role between the user’s compound attitudes and eWOM. The research result can also help the mobile sensor computing industry to develop effective strategies and build strong consumer user—product (brand) relationships. PMID:26999155

Online Social Networking, Sexual Risk and Protective Behaviors: Considerations for Clinicians and Researchers.

PubMed

Holloway, Ian W; Dunlap, Shannon; Del Pino, Homero E; Hermanstyne, Keith; Pulsipher, Craig; Landovitz, Raphael J

2014-09-01

Online social networking refers to the use of internet-based technologies that facilitate connection and communication between users. These platforms may be accessed via computer or mobile device (e.g., tablet, smartphone); communication between users may include linking of profiles, posting of text, photo and video content, instant messaging and email. This review provides an overview of recent research on the relationship between online social networking and sexual risk and protective behaviors with a focus on use of social networking sites (SNS) among young people and populations at high risk for sexually transmitted infections (STIs). While findings are mixed, the widespread use of SNS for sexual communication and partner seeking presents opportunities for the delivery and evaluation of public health interventions. Results of SNS-based interventions to reduce sexual risk are synthesized in order to offer hands-on advice for clinicians and researchers interested in engaging patients and study participants via online social networking.

Adolescent computer use and alcohol use: what are the role of quantity and content of computer use?

PubMed

Epstein, Jennifer A

2011-05-01

The purpose of this study was to examine the relationship between computer use and alcohol use among adolescents. In particular, the goal of the research was to determine the role of lifetime drinking and past month drinking on quantity as measured by amount of time on the computer (for school work and excluding school work) and on content as measured by the frequency of a variety of activities on the internet (e.g., e-mail, searching for information, social networking, listen to/download music). Participants (aged 13-17 years and residing in the United States) were recruited via the internet to complete an anonymous survey online using a popular survey tool (N=270). Their average age was 16 and the sample was predominantly female (63% girls). A series of analyses was conducted with the computer use measures as dependent variables (hours on the computer per week for school work and excluding school work; various internet activities including e-mail, searching for information, social networking, listen to/download music) controlling for gender, age, academic performance and age of first computer use. Based on the results, past month drinkers used the computer more hours per week excluding school work than those who did not. As expected, there were no differences in hours based on alcohol use for computer use for school work. Drinking also had relationships with more frequent social networking and listening to/downloading music. These findings suggest that both quantity and content of computer use were related to adolescent drinking. Copyright © 2010 Elsevier Ltd. All rights reserved.

Feasibility of a computer-assisted social network motivational interviewing intervention for substance use and HIV risk behaviors for housing first residents.

PubMed

Osilla, Karen Chan; Kennedy, David P; Hunter, Sarah B; Maksabedian, Ervant

2016-09-07

Social networks play positive and negative roles in the lives of homeless people influencing their alcohol and/or other drug (AOD) and HIV risk behaviors. We developed a four-session computer-assisted social network motivational interviewing intervention for homeless adults transitioning into housing. We examined the acceptability of the intervention among staff and residents at an organization that provides permanent supportive housing through iterative rounds of beta testing. Staff were 3 men and 3 women who were residential support staff (i.e., case managers and administrators). Residents were 8 men (7 African American, 1 Hispanic) and 3 women (2 African American, 1 Hispanic) who had histories of AOD and HIV risk behaviors. We conducted a focus group with staff who gave input on how to improve the delivery of the intervention to enhance understanding and receptivity among new residents. We conducted semi-structured qualitative interviews and collected self-report satisfaction data from residents. Three themes emerged over the course of the resident interviews. Residents reported that the intervention was helpful in discussing their social network, that seeing the visualizations was more impactful than just talking about their network, and that the intervention prompted thoughts about changing their AOD use and HIV risk networks. This study is the first of its kind that has developed, with input from Housing First staff and residents, a motivational interviewing intervention that targets both the structure and composition of one's social network. These results suggest that providing visual network feedback with a guided motivational interviewing discussion is a promising approach to supporting network change. ClinicalTrials.gov Identifier NCT02140359.

Network integrity of the parental brain in infancy supports the development of children's social competencies.

PubMed

Abraham, Eyal; Hendler, Talma; Zagoory-Sharon, Orna; Feldman, Ruth

2016-11-01

The cross-generational transmission of mammalian sociality, initiated by the parent's postpartum brain plasticity and species-typical behavior that buttress offspring's socialization, has not been studied in humans. In this longitudinal study, we measured brain response of 45 primary-caregiving parents to their infant's stimuli, observed parent-infant interactions, and assayed parental oxytocin (OT). Intra- and inter-network connectivity were computed in three main networks of the human parental brain: core limbic, embodied simulation and mentalizing. During preschool, two key child social competencies were observed: emotion regulation and socialization. Parent's network integrity in infancy predicted preschoolers' social outcomes, with subcortical and cortical network integrity foreshadowing simple evolutionary-based regulatory tactics vs complex self-regulatory strategies and advanced socialization. Parent-infant synchrony mediated the links between connectivity of the parent's embodied simulation network and preschoolers' ability to use cognitive/executive emotion regulation strategies, highlighting the inherently dyadic nature of this network and its long-term effects on tuning young to social life. Parent's inter-network core limbic-embodied simulation connectivity predicted children's OT as moderated by parental OT. Findings challenge solipsistic neuroscience perspectives by demonstrating how the parent-offspring interface enables the brain of one human to profoundly impact long-term adaptation of another. © The Author (2016). Published by Oxford University Press.

Differential reward responses during competition against in- and out-of-network others.

PubMed

Fareri, Dominic S; Delgado, Mauricio R

2014-04-01

Social interactions occur within a variety of different contexts--cooperative/competitive--and often involve members of our social network. Here, we investigated whether social network modulated the value placed on positive outcomes during a competitive context. Eighteen human participants played a simple card-guessing game with three different competitors: a close friend (in-network), a confederate (out-of-network) and a random number generator (non-social condition) while undergoing functional magnetic resonance imaging. Neuroimaging results at the time of outcome receipt demonstrated a significant main effect of competitor across multiple regions of medial prefrontal cortex, with Blood Oxygen Level Dependent (BOLD) responses strongest when competing against one's friend compared with all other conditions. Striatal BOLD responses demonstrated a more general sensitivity to positive compared with negative monetary outcomes, which an exploratory analysis revealed to be stronger when interacting with social, compared with non-social, competitors. Interestingly, a Granger causality analysis indicated directed influences sent from an medial prefrontal cortex (mPFC) region, which shows social network differentiation of outcomes, and the ventral striatum bilaterally. Our results suggest that when competing against others of varying degrees of social network, mPFC differentially values these outcomes, perhaps treating in-network outcomes as more informative, leaving the striatum to more general value computations.

Network integrity of the parental brain in infancy supports the development of children’s social competencies

PubMed Central

Abraham, Eyal; Hendler, Talma; Zagoory-Sharon, Orna

2016-01-01

The cross-generational transmission of mammalian sociality, initiated by the parent’s postpartum brain plasticity and species-typical behavior that buttress offspring’s socialization, has not been studied in humans. In this longitudinal study, we measured brain response of 45 primary-caregiving parents to their infant’s stimuli, observed parent–infant interactions, and assayed parental oxytocin (OT). Intra- and inter-network connectivity were computed in three main networks of the human parental brain: core limbic, embodied simulation and mentalizing. During preschool, two key child social competencies were observed: emotion regulation and socialization. Parent’s network integrity in infancy predicted preschoolers’ social outcomes, with subcortical and cortical network integrity foreshadowing simple evolutionary-based regulatory tactics vs complex self-regulatory strategies and advanced socialization. Parent–infant synchrony mediated the links between connectivity of the parent’s embodied simulation network and preschoolers' ability to use cognitive/executive emotion regulation strategies, highlighting the inherently dyadic nature of this network and its long-term effects on tuning young to social life. Parent’s inter-network core limbic-embodied simulation connectivity predicted children’s OT as moderated by parental OT. Findings challenge solipsistic neuroscience perspectives by demonstrating how the parent–offspring interface enables the brain of one human to profoundly impact long-term adaptation of another. PMID:27369068

Differential reward responses during competition against in- and out-of-network others

PubMed Central

Fareri, Dominic S.

2014-01-01

Social interactions occur within a variety of different contexts––cooperative/competitive––and often involve members of our social network. Here, we investigated whether social network modulated the value placed on positive outcomes during a competitive context. Eighteen human participants played a simple card-guessing game with three different competitors: a close friend (in-network), a confederate (out-of-network) and a random number generator (non-social condition) while undergoing functional magnetic resonance imaging. Neuroimaging results at the time of outcome receipt demonstrated a significant main effect of competitor across multiple regions of medial prefrontal cortex, with Blood Oxygen Level Dependent (BOLD) responses strongest when competing against one’s friend compared with all other conditions. Striatal BOLD responses demonstrated a more general sensitivity to positive compared with negative monetary outcomes, which an exploratory analysis revealed to be stronger when interacting with social, compared with non-social, competitors. Interestingly, a Granger causality analysis indicated directed influences sent from an medial prefrontal cortex (mPFC) region, which shows social network differentiation of outcomes, and the ventral striatum bilaterally. Our results suggest that when competing against others of varying degrees of social network, mPFC differentially values these outcomes, perhaps treating in-network outcomes as more informative, leaving the striatum to more general value computations. PMID:23314007

Dynamic properties of epidemic spreading on finite size complex networks

NASA Astrophysics Data System (ADS)

Li, Ying; Liu, Yang; Shan, Xiu-Ming; Ren, Yong; Jiao, Jian; Qiu, Ben

2005-11-01

The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS (susceptible-infected-susceptible) model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.

Computer Conferencing and Electronic Messaging. Conference Proceedings (Guelph, Ontario, Canada, January 22-23, 1985).

ERIC Educational Resources Information Center

Guelph Univ. (Ontario).

This 21-paper collection examines various issues in electronic networking and conferencing with computers, including design issues, conferencing in education, electronic messaging, computer conferencing applications, social issues of computer conferencing, and distributed computer conferencing. In addition to a keynote address, "Computer…

Computer-Mediated Social Support for Physical Activity: A Content Analysis.

PubMed

Stragier, Jeroen; Mechant, Peter; De Marez, Lieven; Cardon, Greet

2018-02-01

Online fitness communities are a recent phenomenon experiencing growing user bases. They can be considered as online social networks in which recording, monitoring, and sharing of physical activity (PA) are the most prevalent practices. They have added a new dimension to the social experience of PA in which online peers function as virtual PA partners or supporters. However, research into seeking and receiving computer-mediated social support for PA is scarce. Our aim was to study to what extent using online fitness communities and sharing physical activities with online social networks results in receiving various types of online social support. Two databases, one containing physical activities logged with Strava and one containing physical activities logged with RunKeeper and shared on Twitter, were investigated for occurrence and type of social support, by means of a deductive content analysis. Results indicate that social support delivered through Twitter is not particularly extensive. On Strava, social support is significantly more prevalent. Especially esteem support, expressed as compliments for the accomplishment of an activity, is provided on both Strava and Twitter. The results demonstrate that social media have potential as a platform used for providing social support for PA, but differences among various social network sites can be substantial. Especially esteem support can be expected, in contrast to online health communities, where information support is more common.

Crime, Abuse, and Hacker Ethics.

ERIC Educational Resources Information Center

Johnson, Deborah G.

1994-01-01

Discusses computer ethics and the use of computer networks. Topics addressed include computer hackers; software piracy; computer viruses and worms; intentional and unintentional abuse; intellectual property rights versus freedom of thought; the role of information in a democratic society; individual privacy; legislation; social attitudes; and the…

Facebook Usage as a Predictor of Retention at a Private 4-Year Institution

ERIC Educational Resources Information Center

Morris, Jason; Reese, Jeff; Beck, Richard; Mattis, Charles

2010-01-01

Computer-based social networking has become ubiquitous on college and university campuses. However, little is known about how this form of networking reflects social integration which is considered to be an integral component of student persistence. To address this topic, a random sample of 375 entering freshman were used to evaluate the…

Online Social Networks and Computer Skills of University Students

ERIC Educational Resources Information Center

Barbas, Maria Potes; Valerio, Gabriel; Rodríguez-Martínez, María del Carmen; Herrera-Murillo, Dagoberto José; Belmonte-Jiménez, Ana María

2014-01-01

Currently a large number of college students belong to social networks and spend several hours a week on them. Some sectors of society, like parents and teachers, are concerned about the negative impact on their academic work and in their personal lives. However, because the potential positive impacts have not been explored enough, this research…

Colleges Create Facebook-Style Social Networks to Reach Alumni

ERIC Educational Resources Information Center

Hermes, J.J.

2008-01-01

Trying to emulate the popularity of Web sites like Facebook and MySpace, hundreds of college alumni associations have begun to offer their own online social networks, seeking to stake a claim on the computer screens of current and former students, especially young alumni. Many of the sites have struggled to attract alumni and to keep them…

Exploring Social Networking: Developing Critical Literacies

ERIC Educational Resources Information Center

Watson, Pauline

2012-01-01

While schools have been using computers within their classrooms for years now, there has been a purposeful ignoring of the growing power of social networks such as Facebook and Twitter. Many schools ban students from accessing and using sites such as Facebook at school and many English and literacy teachers ignore or deny their value as a teaching…

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level.

PubMed

Coman, Alin; Momennejad, Ida; Drach, Rae D; Geana, Andra

2016-07-19

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members' memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals.

Fast Katz and Commuters: Efficient Estimation of Social Relatedness in Large Networks

NASA Astrophysics Data System (ADS)

Esfandiar, Pooya; Bonchi, Francesco; Gleich, David F.; Greif, Chen; Lakshmanan, Laks V. S.; On, Byung-Won

Motivated by social network data mining problems such as link prediction and collaborative filtering, significant research effort has been devoted to computing topological measures including the Katz score and the commute time. Existing approaches typically approximate all pairwise relationships simultaneously. In this paper, we are interested in computing: the score for a single pair of nodes, and the top-k nodes with the best scores from a given source node. For the pairwise problem, we apply an iterative algorithm that computes upper and lower bounds for the measures we seek. This algorithm exploits a relationship between the Lanczos process and a quadrature rule. For the top-k problem, we propose an algorithm that only accesses a small portion of the graph and is related to techniques used in personalized PageRank computing. To test the scalability and accuracy of our algorithms we experiment with three real-world networks and find that these algorithms run in milliseconds to seconds without any preprocessing.

Fast katz and commuters : efficient estimation of social relatedness in large networks.

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

On, Byung-Won; Lakshmanan, Laks V. S.; Greif, Chen

Motivated by social network data mining problems such as link prediction and collaborative filtering, significant research effort has been devoted to computing topological measures including the Katz score and the commute time. Existing approaches typically approximate all pairwise relationships simultaneously. In this paper, we are interested in computing: the score for a single pair of nodes, and the top-k nodes with the best scores from a given source node. For the pairwise problem, we apply an iterative algorithm that computes upper and lower bounds for the measures we seek. This algorithm exploits a relationship between the Lanczos process and amore » quadrature rule. For the top-k problem, we propose an algorithm that only accesses a small portion of the graph and is related to techniques used in personalized PageRank computing. To test the scalability and accuracy of our algorithms we experiment with three real-world networks and find that these algorithms run in milliseconds to seconds without any preprocessing.« less

The Tethered Self: Technology Reinvents Intimacy and Solitude

ERIC Educational Resources Information Center

Turkle, Sherry

2011-01-01

The author has been a witness to the birth of the personal computer culture, with its intense one-on-one relationships with machines, and then to the development of the networked culture, with people using the computer to communicate with each other. In her most recent work on the revolutions in social networking and sociable robotics, she sees a…

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.

Exploring Social Contextual Correlates of Computer Ownership and Frequency of Use Among Urban, Low-Income, Public Housing Adult Residents

PubMed Central

Puleo, Elaine; Bennett, Gary G; Emmons, Karen M

2007-01-01

Background As advances in computer access continue to be made, there is a need to better understand the challenges of increasing access for racial/ethnic minorities, particularly among those with lower incomes. Larger social contextual factors, such as social networks and neighborhood factors, may influence computer ownership and the number of places where individuals have access to computers. Objectives We examined the associations of sociodemographic and social contextual factors with computer ownership and frequency of use among 1554 adults living in urban public housing. Methods Bivariate associations between dependent variables (computer ownership and regular computer use) and independent variables were used to build multivariable logistic models adjusted for age and site clusters. Results Participants (N = total weighted size of 2270) were on average 51.0 (± 21.4) years old, primarily African American or Hispanic, and earned less than US $20000 per year. More than half owned a computer, and 42% were regular computer users. Reporting computer ownership was more likely if participants lived above the poverty level (OR = 1.78, 95% CI = 1.39-2.29), completed high school (OR = 2.46, 95% CI = 1.70-3.55), were in financial hardship (OR = 1.38, 95% CI = 1.06-1.81), were employed and supervised others (OR = 1.94, 95% CI = 1.08-3.46), and had multiple role responsibilities (OR = 2.18, 95% CI = 1.31-3.61). Regular computer use was more likely if participants were non-Hispanic (OR = 1.94, 95% CI = 1.30-2.91), lived above the poverty level (OR = 2.84, 95% CI = 1.90-4.24), completed high school (OR = 4.43, 95% CI = 3.04-6.46), were employed and supervised others (OR = 2.41, 95% CI = 1.37-4.22), felt safe in their neighborhood (OR = 1.57, 95% CI = 1.08-2.30), and had greater social network ties (OR = 3.09, 95% CI = 1.26-7.59). Conclusions Disparities in computer ownership and use are narrowing, even among those with very low incomes; however, identifying factors that contribute to disparities in access for these groups will be necessary to ensure the efficacy of future technology-based interventions. A unique finding of our study is that it may be equally as important to consider specific social contextual factors when trying to increase access and use among low-income minorities, such as social network ties, household responsibilities, and neighborhood safety. PMID:18093903

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.

Inverse targeting —An effective immunization strategy

NASA Astrophysics Data System (ADS)

Schneider, C. M.; Mihaljev, T.; Herrmann, H. J.

2012-05-01

We propose a new method to immunize populations or computer networks against epidemics which is more efficient than any continuous immunization method considered before. The novelty of our method resides in the way of determining the immunization targets. First we identify those individuals or computers that contribute the least to the disease spreading measured through their contribution to the size of the largest connected cluster in the social or a computer network. The immunization process follows the list of identified individuals or computers in inverse order, immunizing first those which are most relevant for the epidemic spreading. We have applied our immunization strategy to several model networks and two real networks, the Internet and the collaboration network of high-energy physicists. We find that our new immunization strategy is in the case of model networks up to 14%, and for real networks up to 33% more efficient than immunizing dynamically the most connected nodes in a network. Our strategy is also numerically efficient and can therefore be applied to large systems.

Dynamic social community detection and its applications.

PubMed

Nguyen, Nam P; Dinh, Thang N; Shen, Yilin; Thai, My T

2014-01-01

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.

Dynamic Social Community Detection and Its Applications

PubMed Central

Nguyen, Nam P.; Dinh, Thang N.; Shen, Yilin; Thai, My T.

2014-01-01

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods. PMID:24722164

The Influence of Social Networking Technology in an Engineering Organization

ERIC Educational Resources Information Center

Tepaske, Derrick Marcus

2013-01-01

Computer facilitated Social Networking (SN) is becoming more prevalent in our society, both in our personal and professional lives. As its use grows, there is a desire to determine how it will impact an organization. If it can positively impact an organization then it is an initiative that could be embraced and leveraged for any number of business…

Using Centrality of Concept Maps as a Measure of Problem Space States in Computer-Supported Collaborative Problem Solving

ERIC Educational Resources Information Center

Clariana, Roy B.; Engelmann, Tanja; Yu, Wu

2013-01-01

Problem solving likely involves at least two broad stages, problem space representation and then problem solution (Newell and Simon, Human problem solving, 1972). The metric centrality that Freeman ("Social Networks" 1:215-239, 1978) implemented in social network analysis is offered here as a potential measure of both. This development research…

The Functional Neuroanatomy of Human Face Perception.

PubMed

Grill-Spector, Kalanit; Weiner, Kevin S; Kay, Kendrick; Gomez, Jesse

2017-09-15

Face perception is critical for normal social functioning and is mediated by a network of regions in the ventral visual stream. In this review, we describe recent neuroimaging findings regarding the macro- and microscopic anatomical features of the ventral face network, the characteristics of white matter connections, and basic computations performed by population receptive fields within face-selective regions composing this network. We emphasize the importance of the neural tissue properties and white matter connections of each region, as these anatomical properties may be tightly linked to the functional characteristics of the ventral face network. We end by considering how empirical investigations of the neural architecture of the face network may inform the development of computational models and shed light on how computations in the face network enable efficient face perception.

Investigating Sociodemographic Factors and HIV Risk Behaviors Associated With Social Networking Among Adolescents in Soweto, South Africa: A Cross-Sectional Survey.

PubMed

Dietrich, Janan Janine; Laher, Fatima; Hornschuh, Stefanie; Nkala, Busisiwe; Chimoyi, Lucy; Otwombe, Kennedy; Kaida, Angela; Gray, Glenda Elisabeth; Miller, Cari

2016-09-28

Internet access via mobile phones and computers facilitates interaction and potential health communication among individuals through social networking. Many South African adolescents own mobile phones and can access social networks via apps. We investigated sociodemographic factors and HIV risk behaviors of adolescent social networking users in Soweto, South Africa. We conducted an interviewer-administered, cross-sectional survey of adolescents aged 14-19 years. Independent covariates of social networking were assessed by multivariate logistic regression analysis. Of 830 adolescents, 57% (475/830) were females and the median age was found to be 18 years (interquartile range 17-18). Social networking was used by 60% of adolescents (494/830); more than half, that is, 87% (396/494) accessed social networks through mobile phones and 56% (275/494) spent more than 4 hours per day using their mobile phones. Social networking was independently associated with mobile usage 2-4 hours (adjusted odds ratio [AOR]: 3.06, CI: 1.69-5.51) and more than 4 hours per day (AOR: 6.16, CI: 3.46-10.9) and one (AOR: 3.35, CI: 1.79-6.27) or more sexual partner(s) (AOR: 2.58, CI: 1.05-6.36). Mobile phone-based social networking is prevalent among sexually active adolescents living in Soweto and may be used as an entry point for health promotion and initiation of low-cost adolescent health interventions.

Investigating Sociodemographic Factors and HIV Risk Behaviors Associated With Social Networking Among Adolescents in Soweto, South Africa: A Cross-Sectional Survey

PubMed Central

Laher, Fatima; Hornschuh, Stefanie; Nkala, Busisiwe; Chimoyi, Lucy; Otwombe, Kennedy; Kaida, Angela; Gray, Glenda Elisabeth; Miller, Cari

2016-01-01

Background Internet access via mobile phones and computers facilitates interaction and potential health communication among individuals through social networking. Many South African adolescents own mobile phones and can access social networks via apps. Objective We investigated sociodemographic factors and HIV risk behaviors of adolescent social networking users in Soweto, South Africa. Methods We conducted an interviewer-administered, cross-sectional survey of adolescents aged 14-19 years. Independent covariates of social networking were assessed by multivariate logistic regression analysis. Results Of 830 adolescents, 57% (475/830) were females and the median age was found to be 18 years (interquartile range 17-18). Social networking was used by 60% of adolescents (494/830); more than half, that is, 87% (396/494) accessed social networks through mobile phones and 56% (275/494) spent more than 4 hours per day using their mobile phones. Social networking was independently associated with mobile usage 2-4 hours (adjusted odds ratio [AOR]: 3.06, CI: 1.69-5.51) and more than 4 hours per day (AOR: 6.16, CI: 3.46-10.9) and one (AOR: 3.35, CI: 1.79-6.27) or more sexual partner(s) (AOR: 2.58, CI: 1.05-6.36). Conclusions Mobile phone–based social networking is prevalent among sexually active adolescents living in Soweto and may be used as an entry point for health promotion and initiation of low-cost adolescent health interventions. PMID:27683173

Social Networks among the Older Chinese Population in the USA: Findings from the PINE Study.

PubMed

Dong, XinQi; Chang, E-Shien

2017-01-01

Social network research has become central to studies of health and aging. Its results may yield public health insights that are actionable and improve the quality of life of older adults. However, little is known about the social networks of older immigrant adults, whose social relationships often develop in the context of migration, compounded by cultural and linguistic barriers. This report aims to describe the structure, composition, and emotional components of social networks in the Chinese aging population of the USA, and to explore ways in which their social networks may be critical to their health decision-making. Our data come from the PINE study, a population-based epidemiological study of community-dwelling older Chinese American adults, aged 60 years and above, in the greater Chicago area. We conducted individual interviews in participants' homes from 2011 until 2013. Based on sociodemographic and socioeconomic characteristics, this study computed descriptive statistics and trend tests for the social network measures adapted from the National Social Life, Health, and Aging Project study. The findings show that older Chinese adults have a relatively small social network in comparison with their counterparts from other ethnic and racial backgrounds. Only 29.6% of the participants could name 5 close network members, and 2.2% could name 0 members. Their network composition was more heavily kin oriented (95.0%). Relationships with network members differed according to the older adults' sociodemographic and socioeconomic characteristics. Subgroup variations included the likelihood of discussing health-related issues with network members. This study highlights the dynamic nature of social networks in later-life Chinese immigrants. For healthcare practitioners, developing cost-effective strategies that can mobilize social network support remains a critical undertaking in health intervention. Longitudinal studies are needed to examine the causal impact of social networks on various domains of health. © 2017 S. Karger AG, Basel.

Adapting Teaching Strategies To Encompass New Technologies.

ERIC Educational Resources Information Center

Oravec, Jo Ann

2001-01-01

The explosion of special-purpose computing devices--Internet appliances, handheld computers, wireless Internet, networked household appliances--challenges business educators attempting to provide computer literacy education. At a minimum, they should address connectivity, expanded applications, and social and public policy implications of these…

Increasing social capital via local networks: analysis in the context of a surgical practice.

PubMed

Thakur, Anjani; Yang, Isaac; Lee, Michael Y; Goel, Arpan; Ashok, Ashwin; Fonkalsrud, Eric W

2002-09-01

The relationship between social capital (support, trust, patient awareness, and increased practice revenue) and local networks (university hospital) in communities has received little attention. The development of computer-based communication networks (social networks) has added a new dimension to the argument, posing the question of whether local networks can (re-)create social capital in local communities. This relationship is examined through a review of the literature on local networks and social capital and a surgeon's practice management from 1990 to 2001 with respect to repair of pectus chest deformities. With respect to pectus repair there was a consistent but small number of new referrals (15-20 new patients/year), lack of patient awareness (eight to 12 self-referred patients/year), and modest practice revenue. Since the inception of an Internet website (social network) dedicated to pectus repair in 1996 there has been increased social participation (n = 630 hits/year to the website); facilitation of spread of information through E-mail messages (n = 430 messages/year); and a greater participation of groups such as women, minorities, adults, and those with disability (n = 120 patients/year). The dissemination of information via the local network has also allowed an "outward movement" with increased participation by interconnecting communities (n = 698,300 global Internet participants based on statistical ratios). We conclude that local networks have enhanced social networks providing new grounds for the development of relationships based on choice and shared interest.

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.

Energy Efficiency in Public Buildings through Context-Aware Social Computing.

PubMed

García, Óscar; Alonso, Ricardo S; Prieto, Javier; Corchado, Juan M

2017-04-11

The challenge of promoting behavioral changes in users that leads to energy savings in public buildings has become a complex task requiring the involvement of multiple technologies. Wireless sensor networks have a great potential for the development of tools, such as serious games, that encourage acquiring good energy and healthy habits among users in the workplace. This paper presents the development of a serious game using CAFCLA, a framework that allows for integrating multiple technologies, which provide both context-awareness and social computing. Game development has shown that the data provided by sensor networks encourage users to reduce energy consumption in their workplace and that social interactions and competitiveness allow for accelerating the achievement of good results and behavioral changes that favor energy savings.

Social network size can influence linguistic malleability and the propagation of linguistic change.

PubMed

Lev-Ari, Shiri

2018-07-01

We learn language from our social environment, but the more sources we have, the less informative each source is, and therefore, the less weight we ascribe its input. According to this principle, people with larger social networks should give less weight to new incoming information, and should therefore be less susceptible to the influence of new speakers. This paper tests this prediction, and shows that speakers with smaller social networks indeed have more malleable linguistic representations. In particular, they are more likely to adjust their lexical boundary following exposure to a new speaker. Experiment 2 uses computational simulations to test whether this greater malleability could lead people with smaller social networks to be important for the propagation of linguistic change despite the fact that they interact with fewer people. The results indicate that when innovators were connected with people with smaller rather than larger social networks, the population exhibited greater and faster diffusion. Together these experiments show that the properties of people's social networks can influence individuals' learning and use as well as linguistic phenomena at the community level. Copyright © 2018 Elsevier B.V. All rights reserved.

Language Networks as Complex Systems

ERIC Educational Resources Information Center

Lee, Max Kueiming; Ou, Sheue-Jen

2008-01-01

Starting in the late eighties, with a growing discontent with analytical methods in science and the growing power of computers, researchers began to study complex systems such as living organisms, evolution of genes, biological systems, brain neural networks, epidemics, ecology, economy, social networks, etc. In the early nineties, the research…

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.

Multi-Relational Characterization of Dynamic Social Network Communities

NASA Astrophysics Data System (ADS)

Lin, Yu-Ru; Sundaram, Hari; Kelliher, Aisling

The emergence of the mediated social web - a distributed network of participants creating rich media content and engaging in interactive conversations through Internet-based communication technologies - has contributed to the evolution of powerful social, economic and cultural change. Online social network sites and blogs, such as Facebook, Twitter, Flickr and LiveJournal, thrive due to their fundamental sense of "community". The growth of online communities offers both opportunities and challenges for researchers and practitioners. Participation in online communities has been observed to influence people's behavior in diverse ways ranging from financial decision-making to political choices, suggesting the rich potential for diverse applications. However, although studies on the social web have been extensive, discovering communities from online social media remains challenging, due to the interdisciplinary nature of this subject. In this article, we present our recent work on characterization of communities in online social media using computational approaches grounded on the observations from social science.

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 Networking, Sexual Risk and Protective Behaviors: Considerations for Clinicians and Researchers

PubMed Central

Holloway, Ian W.; Dunlap, Shannon; del Pino, Homero E.; Hermanstyne, Keith; Pulsipher, Craig; Landovitz, Raphael J.

2014-01-01

Online social networking refers to the use of internet-based technologies that facilitate connection and communication between users. These platforms may be accessed via computer or mobile device (e.g., tablet, smartphone); communication between users may include linking of profiles, posting of text, photo and video content, instant messaging and email. This review provides an overview of recent research on the relationship between online social networking and sexual risk and protective behaviors with a focus on use of social networking sites (SNS) among young people and populations at high risk for sexually transmitted infections (STIs). While findings are mixed, the widespread use of SNS for sexual communication and partner seeking presents opportunities for the delivery and evaluation of public health interventions. Results of SNS-based interventions to reduce sexual risk are synthesized in order to offer hands-on advice for clinicians and researchers interested in engaging patients and study participants via online social networking. PMID:25642408

Auxiliary Parameter MCMC for Exponential Random Graph Models

NASA Astrophysics Data System (ADS)

Byshkin, Maksym; Stivala, Alex; Mira, Antonietta; Krause, Rolf; Robins, Garry; Lomi, Alessandro

2016-11-01

Exponential random graph models (ERGMs) are a well-established family of statistical models for analyzing social networks. Computational complexity has so far limited the appeal of ERGMs for the analysis of large social networks. Efficient computational methods are highly desirable in order to extend the empirical scope of ERGMs. In this paper we report results of a research project on the development of snowball sampling methods for ERGMs. We propose an auxiliary parameter Markov chain Monte Carlo (MCMC) algorithm for sampling from the relevant probability distributions. The method is designed to decrease the number of allowed network states without worsening the mixing of the Markov chains, and suggests a new approach for the developments of MCMC samplers for ERGMs. We demonstrate the method on both simulated and actual (empirical) network data and show that it reduces CPU time for parameter estimation by an order of magnitude compared to current MCMC methods.

A Research into the Pedagogic Formation, Foreign Language and Computer Related Knowledge of Track and Field Trainers in Turkey

ERIC Educational Resources Information Center

Pekel, Haci Ahmet

2014-01-01

Foreign language, computer programs and social network applications or web sites are widely used by many people nowadays for various aims. In the literature, the number of studies investigating over university departments of physical education or more specifically to say, taking sports students' and teachers' foreign language and social networking…

Quantifiable and objective approach to organizational performance enhancement.

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

Scholand, Andrew Joseph; Tausczik, Yla R.

This report describes a new methodology, social language network analysis (SLNA), that combines tools from social language processing and network analysis to identify socially situated relationships between individuals which, though subtle, are highly influential. Specifically, SLNA aims to identify and characterize the nature of working relationships by processing artifacts generated with computer-mediated communication systems, such as instant message texts or emails. Because social language processing is able to identify psychological, social, and emotional processes that individuals are not able to fully mask, social language network analysis can clarify and highlight complex interdependencies between group members, even when these relationships aremore » latent or unrecognized. This report outlines the philosophical antecedents of SLNA, the mechanics of preprocessing, processing, and post-processing stages, and some example results obtained by applying this approach to a 15-month corporate discussion archive.« less

He Votes or She Votes? Female and Male Discursive Strategies in Twitter Political Hashtags

PubMed Central

Cunha, Evandro; Magno, Gabriel; Gonçalves, Marcos André; Cambraia, César; Almeida, Virgilio

2014-01-01

In this paper, we conduct a study about differences between female and male discursive strategies when posting in the microblogging service Twitter, with a particular focus on the hashtag designation process during political debate. The fact that men and women use language in distinct ways, reverberating practices linked to their expected roles in the social groups, is a linguistic phenomenon known to happen in several cultures and that can now be studied on the Web and on online social networks in a large scale enabled by computing power. Here, for instance, after analyzing tweets with political content posted during Brazilian presidential campaign,we found out that male Twitter users, when expressing their attitude toward a given candidate, are more prone to use imperative verbal forms in hashtags, while female users tend to employ declarative forms. This difference can be interpreted as a sign of distinct approaches in relation to other network members: for example, if political hashtags are seen as strategies of persuasion in Twitter, imperative tags could be understood as more overt ways of persuading and declarative tags as more indirect ones. Our findings help to understand human gendered behavior in social networks and contribute to research on the new fields of computer-enabled Internet linguistics and social computing, besides being useful for several computational tasks such as developing tag recommendation systems based on users' collective preferences and tailoring targeted advertising strategies, among others. PMID:24489832

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level

PubMed Central

Coman, Alin; Momennejad, Ida; Drach, Rae D.; Geana, Andra

2016-01-01

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members’ memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals. PMID:27357678

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

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

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.

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.

Energy Efficiency in Public Buildings through Context-Aware Social Computing

PubMed Central

García, Óscar; Alonso, Ricardo S.; Prieto, Javier; Corchado, Juan M.

2017-01-01

The challenge of promoting behavioral changes in users that leads to energy savings in public buildings has become a complex task requiring the involvement of multiple technologies. Wireless sensor networks have a great potential for the development of tools, such as serious games, that encourage acquiring good energy and healthy habits among users in the workplace. This paper presents the development of a serious game using CAFCLA, a framework that allows for integrating multiple technologies, which provide both context-awareness and social computing. Game development has shown that the data provided by sensor networks encourage users to reduce energy consumption in their workplace and that social interactions and competitiveness allow for accelerating the achievement of good results and behavioral changes that favor energy savings. PMID:28398237

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

Private algorithms for the protected in social network search

PubMed Central

Kearns, Michael; Roth, Aaron; Wu, Zhiwei Steven; Yaroslavtsev, Grigory

2016-01-01

Motivated by tensions between data privacy for individual citizens and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a computational model that distinguishes between parties for whom privacy is explicitly protected, and those for whom it is not (the targeted subpopulation). The goal is the development of algorithms that can effectively identify and take action upon members of the targeted subpopulation in a way that minimally compromises the privacy of the protected, while simultaneously limiting the expense of distinguishing members of the two groups via costly mechanisms such as surveillance, background checks, or medical testing. Within this framework, we provide provably privacy-preserving algorithms for targeted search in social networks. These algorithms are natural variants of common graph search methods, and ensure privacy for the protected by the careful injection of noise in the prioritization of potential targets. We validate the utility of our algorithms with extensive computational experiments on two large-scale social network datasets. PMID:26755606

Private algorithms for the protected in social network search.

PubMed

Kearns, Michael; Roth, Aaron; Wu, Zhiwei Steven; Yaroslavtsev, Grigory

2016-01-26

Motivated by tensions between data privacy for individual citizens and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a computational model that distinguishes between parties for whom privacy is explicitly protected, and those for whom it is not (the targeted subpopulation). The goal is the development of algorithms that can effectively identify and take action upon members of the targeted subpopulation in a way that minimally compromises the privacy of the protected, while simultaneously limiting the expense of distinguishing members of the two groups via costly mechanisms such as surveillance, background checks, or medical testing. Within this framework, we provide provably privacy-preserving algorithms for targeted search in social networks. These algorithms are natural variants of common graph search methods, and ensure privacy for the protected by the careful injection of noise in the prioritization of potential targets. We validate the utility of our algorithms with extensive computational experiments on two large-scale social network datasets.

Final Report: Sensorpedia Phases 1 and 2

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

Gorman, Bryan L; Resseguie, David R

2010-08-01

Over the past several years, ORNL has been actively involved in research to formalize the engineering principles and best practices behind emerging social media and social networking concepts to solve real-time data sharing problems for national security and defense, public health and safety, environmental and infrastructure awareness, and disaster preparedness and response. Sensorpedia, an ORNL web site, is a practical application of several key social media principles. Dubbed the Wikipedia for sensors, Sensorpedia is currently in limited BETA testing and was selected in 2009 by Federal Computer Week as one of the government s top 10 social networking sites.

Social Knowledge Awareness Map for Computer Supported Ubiquitous Learning Environment

ERIC Educational Resources Information Center

El-Bishouty, Moushir M.; Ogata, Hiroaki; Rahman, Samia; Yano, Yoneo

2010-01-01

Social networks are helpful for people to solve problems by providing useful information. Therefore, the importance of mobile social software for learning has been supported by many researches. In this research, a model of personalized collaborative ubiquitous learning environment is designed and implemented in order to support learners doing…

Analysis on the University’s Network Security Level System in the Big Data Era

NASA Astrophysics Data System (ADS)

Li, Tianli

2017-12-01

The rapid development of science and technology, the continuous expansion of the scope of computer network applications, has gradually improved the social productive forces, has had a positive impact on the increase production efficiency and industrial scale of China's different industries. Combined with the actual application of computer network in the era of large data, we can see the existence of influencing factors such as network virus, hacker and other attack modes, threatening network security and posing a potential threat to the safe use of computer network in colleges and universities. In view of this unfavorable development situation, universities need to pay attention to the analysis of the situation of large data age, combined with the requirements of network security use, to build a reliable network space security system from the equipment, systems, data and other different levels. To avoid the security risks exist in the network. Based on this, this paper will analyze the hierarchical security system of cyberspace security in the era of large data.

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.

Topology Analysis of Social Networks Extracted from Literature

PubMed Central

2015-01-01

In a world where complex networks are an increasingly important part of science, it is interesting to question how the new reading of social realities they provide applies to our cultural background and in particular, popular culture. Are authors of successful novels able to reproduce social networks faithful to the ones found in reality? Is there any common trend connecting an author’s oeuvre, or a genre of fiction? Such an analysis could provide new insight on how we, as a culture, perceive human interactions and consume media. The purpose of the work presented in this paper is to define the signature of a novel’s story based on the topological analysis of its social network of characters. For this purpose, an automated tool was built that analyses the dialogs in novels, identifies characters and computes their relationships in a time-dependent manner in order to assess the network’s evolution over the course of the story. PMID:26039072

Topographical Organization of Attentional, Social, and Memory Processes in the Human Temporoparietal Cortex123

PubMed Central

Webb, Taylor W.; Kelly, Yin T.; Graziano, Michael S. A.

2016-01-01

Abstract The temporoparietal junction (TPJ) is activated in association with a large range of functions, including social cognition, episodic memory retrieval, and attentional reorienting. An ongoing debate is whether the TPJ performs an overarching, domain-general computation, or whether functions reside in domain-specific subdivisions. We scanned subjects with fMRI during five tasks known to activate the TPJ, probing social, attentional, and memory functions, and used data-driven parcellation (independent component analysis) to isolate task-related functional processes in the bilateral TPJ. We found that one dorsal component in the right TPJ, which was connected with the frontoparietal control network, was activated in all of the tasks. Other TPJ subregions were specific for attentional reorienting, oddball target detection, or social attribution of belief. The TPJ components that participated in attentional reorienting and oddball target detection appeared spatially separated, but both were connected with the ventral attention network. The TPJ component that participated in the theory-of-mind task was part of the default-mode network. Further, we found that the BOLD response in the domain-general dorsal component had a longer latency than responses in the domain-specific components, suggesting an involvement in distinct, perhaps postperceptual, computations. These findings suggest that the TPJ performs both domain-general and domain-specific computations that reside within spatially distinct functional components. PMID:27280153

Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks

PubMed Central

Piraveenan, Mahendra; Prokopenko, Mikhail; Hossain, Liaquat

2013-01-01

A number of centrality measures are available to determine the relative importance of a node in a complex network, and betweenness is prominent among them. However, the existing centrality measures are not adequate in network percolation scenarios (such as during infection transmission in a social network of individuals, spreading of computer viruses on computer networks, or transmission of disease over a network of towns) because they do not account for the changing percolation states of individual nodes. We propose a new measure, percolation centrality, that quantifies relative impact of nodes based on their topological connectivity, as well as their percolation states. The measure can be extended to include random walk based definitions, and its computational complexity is shown to be of the same order as that of betweenness centrality. We demonstrate the usage of percolation centrality by applying it to a canonical network as well as simulated and real world scale-free and random networks. PMID:23349699

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

Exploring biological and social networks to better understand and treat diabetes mellitus. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

NASA Astrophysics Data System (ADS)

Belgardt, Bengt-Frederik; Jarasch, Alexander; Lammert, Eckhard

2018-03-01

Improvements and breakthroughs in computational sciences in the last 20 years have paralleled the rapid gain of influence of social networks on our daily life. As timely reviewed by Perc and colleagues [1], understanding and treating complex human diseases, such as type 2 diabetes (T2D), from which already more than 5% of the global population suffer, will necessitate analyzing and understanding the multi-layered and interconnected networks that usually keep physiological functions intact, but are disturbed in disease states. These networks range from intra- and intercellular networks influencing cell behavior (e.g., secretion of insulin in response to food intake and anabolic response to insulin) to social networks influencing human behavior (e.g., food intake and physical activity). This commentary first expands on the background of pancreatic beta cell networks in human health and T2D, briefly introduces exosomes as novel signals exchanged between distant cellular networks, and finally discusses potential pitfalls and chances in network analyses with regards to experimental data acquisition and processing.

Adolescent Problematic Social Networking and School Experiences: The Mediating Effects of Sleep Disruptions and Sleep Quality.

PubMed

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

2015-07-01

An important developmental task for adolescents is to become increasingly responsible for their own health behaviors. Establishing healthy sleep routines and controlling media use before bedtime are important for adequate, quality sleep so adolescents are alert during the day and perform well at school. Despite the prevalence of adolescent social media use and the large percentage of computers and cell phones in adolescents' bedrooms, no studies to date have investigated the link between problematic adolescent investment in social networking, their sleep practices, and associated experiences at school. A sample of 1,886 students in Australia aged between 12 and 18 years of age completed self-report data on problematic social networking use, sleep disturbances, sleep quality, and school satisfaction. Structural equation modeling (SEM) substantiated the serial mediation hypothesis: for adolescents, problematic social networking use significantly increased sleep disturbances, which adversely affected perceptions of sleep quality that, in turn, lowered adolescents' appraisals of their school satisfaction. This significant pattern was largely driven by the indirect effect of sleep disturbances. These findings suggest that adolescents are vulnerable to negative consequences from social networking use. Specifically, problematic social networking is associated with poor school experiences, which result from poor sleep habits. Promoting better sleep routines by minimizing sleep disturbances from social media use could improve school experiences for adolescents with enhanced emotional engagement and improved subjective well-being.

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.

A Survey of Models and Algorithms for Social Influence Analysis

NASA Astrophysics Data System (ADS)

Sun, Jimeng; Tang, Jie

Social influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social influence has been a widely accepted phenomenon in social networks for decades. Many applications have been built based around the implicit notation of social influence between people, such as marketing, advertisement and recommendations. With the exponential growth of online social network services such as Facebook and Twitter, social influence can for the first time be measured over a large population. In this chapter, we survey the research on social influence analysis with a focus on the computational aspects. First, we present statistical measurements related to social influence. Second, we describe the literature on social similarity and influences. Third, we present the research on social influence maximization which has many practical applications including marketing and advertisement.

Digital books.

PubMed

Wink, Diane M

2011-01-01

In this bimonthly series, the author examines how nurse educators can use the Internet and Web-based computer technologies such as search, communication, and collaborative writing tools; social networking and social bookmarking sites; virtual worlds; and Web-based teaching and learning programs. This article describes digital books.

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.

A model for evolution of overlapping community networks

NASA Astrophysics Data System (ADS)

Karan, Rituraj; Biswal, Bibhu

2017-05-01

A model is proposed for the evolution of network topology in social networks with overlapping community structure. Starting from an initial community structure that is defined in terms of group affiliations, the model postulates that the subsequent growth and loss of connections is similar to the Hebbian learning and unlearning in the brain and is governed by two dominant factors: the strength and frequency of interaction between the members, and the degree of overlap between different communities. The temporal evolution from an initial community structure to the current network topology can be described based on these two parameters. It is possible to quantify the growth occurred so far and predict the final stationary state to which the network is likely to evolve. Applications in epidemiology or the spread of email virus in a computer network as well as finding specific target nodes to control it are envisaged. While facing the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities one faces the most basic questions: how do communities evolve in time? This work aims to address this issue by developing a mathematical model for the evolution of community networks and studying it through computer simulation.

Contributions of Traditional Web 1.0 Tools e.g. Email and Web 2.0 Tools e.g. Weblog towards Knowledge Management

ERIC Educational Resources Information Center

Dehinbo, Johnson

2010-01-01

The use of email utilizes the power of Web 1.0 to enable users to access their email from any computer and mobile devices that is connected to the Internet making email valuable in acquiring and transferring knowledge. But the advent of Web 2.0 and social networking seems to indicate certain limitations of email. The use of social networking seems…

Nonlinearity in Social Service Evaluation: A Primer on Agent-Based Modeling

ERIC Educational Resources Information Center

Israel, Nathaniel; Wolf-Branigin, Michael

2011-01-01

Measurement of nonlinearity in social service research and evaluation relies primarily on spatial analysis and, to a lesser extent, social network analysis. Recent advances in geographic methods and computing power, however, allow for the greater use of simulation methods. These advances now enable evaluators and researchers to simulate complex…

Computer-Mediated Social Support for Physical Activity: A Content Analysis

ERIC Educational Resources Information Center

Stragier, Jeroen; Mechant, Peter; De Marez, Lieven; Cardon, Greet

2018-01-01

Purpose: Online fitness communities are a recent phenomenon experiencing growing user bases. They can be considered as online social networks in which recording, monitoring, and sharing of physical activity (PA) are the most prevalent practices. They have added a new dimension to the social experience of PA in which online peers function as…

Fast Ss-Ilm a Computationally Efficient Algorithm to Discover Socially Important Locations

NASA Astrophysics Data System (ADS)

Dokuz, A. S.; Celik, M.

2017-11-01

Socially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provide several valuable information about user behaviours on social media networking sites. However, discovering socially important locations are challenging due to data volume and dimensions, spatial and temporal calculations, location sparseness in social media datasets, and inefficiency of current algorithms. In the literature, several studies are conducted to discover important locations, however, the proposed approaches do not work in computationally efficient manner. In this study, we propose Fast SS-ILM algorithm by modifying the algorithm of SS-ILM to mine socially important locations efficiently. Experimental results show that proposed Fast SS-ILM algorithm decreases execution time of socially important locations discovery process up to 20 %.

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.

The Future of School Library Media Centers.

ERIC Educational Resources Information Center

Craver, Kathleen W.

1984-01-01

Examines impact of technology on school library media program development and role of school librarian. Technological trends (computerized record keeping, computer-assisted instruction, networking, home computers, videodiscs), employment and economic trends, education of school librarians, social and behavioral trends, and organizational and…

Dynamical networks of influence in small group discussions.

PubMed

Moussaïd, Mehdi; Noriega Campero, Alejandro; Almaatouq, Abdullah

2018-01-01

In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network-a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences.

Cybersim: geographic, temporal, and organizational dynamics of malware propagation

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

Santhi, Nandakishore; Yan, Guanhua; Eidenbenz, Stephan

2010-01-01

Cyber-infractions into a nation's strategic security envelope pose a constant and daunting challenge. We present the modular CyberSim tool which has been developed in response to the need to realistically simulate at a national level, software vulnerabilities and resulting mal ware propagation in online social networks. CyberSim suite (a) can generate realistic scale-free networks from a database of geocoordinated computers to closely model social networks arising from personal and business email contacts and online communities; (b) maintains for each,bost a list of installed software, along with the latest published vulnerabilities; (d) allows designated initial nodes where malware gets introduced; (e)more » simulates, using distributed discrete event-driven technology, the spread of malware exploiting a specific vulnerability, with packet delay and user online behavior models; (f) provides a graphical visualization of spread of infection, its severity, businesses affected etc to the analyst. We present sample simulations on a national level network with millions of computers.« less

Using the Computer to Foster Creative Interaction among Students.

ERIC Educational Resources Information Center

Dugdale, Sharon

The network characteristics of the PLATO computer-based education system permit students to communicate not only with the computer, but with each other. This capability can be exploited in educationally significant ways. In addition to the social interaction which occurs when students help each other at the terminal and when they work together at…

Teaching Hackers: School Computing Culture and the Future of Cyber-Rights.

ERIC Educational Resources Information Center

Van Buren, Cassandra

2001-01-01

Discussion of the need for ethical computing strategies and policies at the K-12 level to acculturate computer hackers away from malicious network hacking focuses on a three-year participant observation ethnographic study conducted at the New Technology High School (California) that examined the school's attempts to socialize its hackers to act…

Sexual risk and HIV prevention behaviours among African-American and Latino MSM social networking users.

PubMed

Young, Sean D; Szekeres, Greg; Coates, Thomas

2013-08-01

This study explores the feasibility of recruiting minority men who have sex with men Facebook users for human immunodeficiency virus (HIV) prevention studies and notes demographic and sexual risk behaviours. Facebook-registered men who have sex with men (MSM; N = 118) were recruited using online and offline methods. Participants validated Facebook-user status through using a Facebook Connect (computer science) application. Participants were primarily Latino (60.2%) and African-American (28.0%), with 33.1% using social media to find sex partners. Black MSM social networking users reported engaging in a lower frequency (coefficient = -0.48, p < 0.05) of unprotected receptive anal intercourse compared to Latino MSM. Results suggest that minority social media users can be recruited for HIV studies and that sexual risk behavioural differences exist among minority social networking users. Findings highlight the importance of incorporating technologies into population-focused HIV interventions.

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.

OxyContin® as Currency: OxyContin® Use and Increased Social Capital among Rural Appalachian Drug Users

PubMed Central

Jonas, Adam B.; Young, April M.; Oser, Carrie B.; Leukefeld, Carl G.; Havens, Jennifer R.

2012-01-01

Studies have shown that position within networks of social relations can have direct implications on the health behaviors of individuals. The present study examines connections between drug use and individual social capital within social networks of drug users (n=503) from rural Appalachian Kentucky, U.S.A. Respondent driven sampling was used to recruit individuals age 18 and older who had used one of the following drugs to get high: cocaine, crack, heroin, methamphetamine, or prescription opioids. Substance use was measured via self-report and social network analysis of participants’ drug use network was used to compute effective size, a measure of social capital. Drug network ties were based on sociometric data on recent (past 6 month) drug co-usage. Multivariate multi-level ordinal regression was used to model the independent effect of sociodemographic and drug use characteristics on social capital. Adjusting for gender, income, and education, daily OxyContin® use was found to be significantly associated with greater social capital, and daily marijuana use was associated with less social capital. These results suggest that in regions with marked economic disparities such as rural Appalachia, OxyContin® may serve as a form of currency that is associated with increased social capital among drug users. Interventions focusing on increasing alternate pathways to acquiring social capital may be one way in which to alleviate the burden of drug use in this high-risk population. PMID:22465379

Rapid innovation diffusion in social networks.

PubMed

Kreindler, Gabriel E; Young, H Peyton

2014-07-22

Social and technological innovations often spread through social networks as people respond to what their neighbors are doing. Previous research has identified specific network structures, such as local clustering, that promote rapid diffusion. Here we derive bounds that are independent of network structure and size, such that diffusion is fast whenever the payoff gain from the innovation is sufficiently high and the agents' responses are sufficiently noisy. We also provide a simple method for computing an upper bound on the expected time it takes for the innovation to become established in any finite network. For example, if agents choose log-linear responses to what their neighbors are doing, it takes on average less than 80 revision periods for the innovation to diffuse widely in any network, provided that the error rate is at least 5% and the payoff gain (relative to the status quo) is at least 150%. Qualitatively similar results hold for other smoothed best-response functions and populations that experience heterogeneous payoff shocks.

Rapid innovation diffusion in social networks

PubMed Central

Kreindler, Gabriel E.; Young, H. Peyton

2014-01-01

Social and technological innovations often spread through social networks as people respond to what their neighbors are doing. Previous research has identified specific network structures, such as local clustering, that promote rapid diffusion. Here we derive bounds that are independent of network structure and size, such that diffusion is fast whenever the payoff gain from the innovation is sufficiently high and the agents’ responses are sufficiently noisy. We also provide a simple method for computing an upper bound on the expected time it takes for the innovation to become established in any finite network. For example, if agents choose log-linear responses to what their neighbors are doing, it takes on average less than 80 revision periods for the innovation to diffuse widely in any network, provided that the error rate is at least 5% and the payoff gain (relative to the status quo) is at least 150%. Qualitatively similar results hold for other smoothed best-response functions and populations that experience heterogeneous payoff shocks. PMID:25024191

Influence versus intent for predictive analytics in situation awareness

NASA Astrophysics Data System (ADS)

Cui, Biru; Yang, Shanchieh J.; Kadar, Ivan

2013-05-01

Predictive analytics in situation awareness requires an element to comprehend and anticipate potential adversary activities that might occur in the future. Most work in high level fusion or predictive analytics utilizes machine learning, pattern mining, Bayesian inference, and decision tree techniques to predict future actions or states. The emergence of social computing in broader contexts has drawn interests in bringing the hypotheses and techniques from social theory to algorithmic and computational settings for predictive analytics. This paper aims at answering the question on how influence and attitude (some interpreted such as intent) of adversarial actors can be formulated and computed algorithmically, as a higher level fusion process to provide predictions of future actions. The challenges in this interdisciplinary endeavor include drawing existing understanding of influence and attitude in both social science and computing fields, as well as the mathematical and computational formulation for the specific context of situation to be analyzed. The study of `influence' has resurfaced in recent years due to the emergence of social networks in the virtualized cyber world. Theoretical analysis and techniques developed in this area are discussed in this paper in the context of predictive analysis. Meanwhile, the notion of intent, or `attitude' using social theory terminologies, is a relatively uncharted area in the computing field. Note that a key objective of predictive analytics is to identify impending/planned attacks so their `impact' and `threat' can be prevented. In this spirit, indirect and direct observables are drawn and derived to infer the influence network and attitude to predict future threats. This work proposes an integrated framework that jointly assesses adversarial actors' influence network and their attitudes as a function of past actions and action outcomes. A preliminary set of algorithms are developed and tested using the Global Terrorism Database (GTD). Our results reveals the benefits to perform joint predictive analytics with both attitude and influence. At the same time, we discover significant challenges in deriving influence and attitude from indirect observables for diverse adversarial behavior. These observations warrant further investigation of optimal use of influence and attitude for predictive analytics, as well as the potential inclusion of other environmental or capability elements for the actors.

Social Software and National Security: An Initial Net Assessment

DTIC Science & Technology

2009-04-01

networks. Government ignores this fact at its peril. Use of social software as ICT is creative and collaborative. Large corporations conduct...from the collaborative, distributed approaches promoted by responsible use of social software. Our recommendations are not exhaustive, but this... responsibilities are there for cyber security when using social software on government computers in a Web 2.0 environment?   67 This section might be

State of the Art of Network Security Perspectives in Cloud Computing

NASA Astrophysics Data System (ADS)

Oh, Tae Hwan; Lim, Shinyoung; Choi, Young B.; Park, Kwang-Roh; Lee, Heejo; Choi, Hyunsang

Cloud computing is now regarded as one of social phenomenon that satisfy customers' needs. It is possible that the customers' needs and the primary principle of economy - gain maximum benefits from minimum investment - reflects realization of cloud computing. We are living in the connected society with flood of information and without connected computers to the Internet, our activities and work of daily living will be impossible. Cloud computing is able to provide customers with custom-tailored features of application software and user's environment based on the customer's needs by adopting on-demand outsourcing of computing resources through the Internet. It also provides cloud computing users with high-end computing power and expensive application software package, and accordingly the users will access their data and the application software where they are located at the remote system. As the cloud computing system is connected to the Internet, network security issues of cloud computing are considered as mandatory prior to real world service. In this paper, survey and issues on the network security in cloud computing are discussed from the perspective of real world service environments.

Dataset on the learning performance of ECDL digital skills of undergraduate students for comparing educational gaming, gamification and social networking.

PubMed

de-Marcos, Luis; García-López, Eva; García-Cabot, Antonio

2017-04-01

This paper reports data about the learning performance of students using four different motivational tools: an educational game, a gamified plugin, a social networking website and a gamified social networking website. It also reports a control group. The data pertain to 379 students of an undergraduate course that covers basic Information and Communication Technology (ICT) skills in Spain. Data corresponds to different learning modules of the European Computer Driving License (ECDL) initiative. The data include variables of four pre-test scores, four post-test scores and a final examination. It was gathered using a quasi-experimental research design during 2014. Data reported here refers to the research paper in (de-Marcos et al., 2016) [1].

Network neuroscience

PubMed Central

Bassett, Danielle S; Sporns, Olaf

2017-01-01

Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system. PMID:28230844

Statistically validated mobile communication networks: the evolution of motifs in European and Chinese data

NASA Astrophysics Data System (ADS)

Li, Ming-Xia; Palchykov, Vasyl; Jiang, Zhi-Qiang; Kaski, Kimmo; Kertész, János; Miccichè, Salvatore; Tumminello, Michele; Zhou, Wei-Xing; Mantegna, Rosario N.

2014-08-01

Big data open up unprecedented opportunities for investigating complex systems, including society. In particular, communication data serve as major sources for computational social sciences, but they have to be cleaned and filtered as they may contain spurious information due to recording errors as well as interactions, like commercial and marketing activities, not directly related to the social network. The network constructed from communication data can only be considered as a proxy for the network of social relationships. Here we apply a systematic method, based on multiple-hypothesis testing, to statistically validate the links and then construct the corresponding Bonferroni network, generalized to the directed case. We study two large datasets of mobile phone records, one from Europe and the other from China. For both datasets we compare the raw data networks with the corresponding Bonferroni networks and point out significant differences in the structures and in the basic network measures. We show evidence that the Bonferroni network provides a better proxy for the network of social interactions than the original one. Using the filtered networks, we investigated the statistics and temporal evolution of small directed 3-motifs and concluded that closed communication triads have a formation time scale, which is quite fast and typically intraday. We also find that open communication triads preferentially evolve into other open triads with a higher fraction of reciprocated calls. These stylized facts were observed for both datasets.

Socio-inspired ICT. Towards a socially grounded society-ICT symbiosis

NASA Astrophysics Data System (ADS)

Ferscha, A.; Farrahi, K.; van den Hoven, J.; Hales, D.; Nowak, A.; Lukowicz, P.; Helbing, D.

2012-11-01

Modern ICT (Information and Communication Technology) has developed a vision where the "computer" is no longer associated with the concept of a single device or a network of devices, but rather the entirety of situated services originating in a digital world, which are perceived through the physical world. It is observed that services with explicit user input and output are becoming to be replaced by a computing landscape sensing the physical world via a huge variety of sensors, and controlling it via a plethora of actuators. The nature and appearance of computing devices is changing to be hidden in the fabric of everyday life, invisibly networked, and omnipresent, with applications greatly being based on the notions of context and knowledge. Interaction with such globe spanning, modern ICT systems will presumably be more implicit, at the periphery of human attention, rather than explicit, i.e. at the focus of human attention.Socio-inspired ICT assumes that future, globe scale ICT systems should be viewed as social systems. Such a view challenges research to identify and formalize the principles of interaction and adaptation in social systems, so as to be able to ground future ICT systems on those principles. This position paper therefore is concerned with the intersection of social behaviour and modern ICT, creating or recreating social conventions and social contexts through the use of pervasive, globe-spanning, omnipresent and participative ICT.

Gossip in Random Networks

NASA Astrophysics Data System (ADS)

Malarz, K.; Szvetelszky, Z.; Szekf, B.; Kulakowski, K.

2006-11-01

We consider the average probability X of being informed on a gossip in a given social network. The network is modeled within the random graph theory of Erd{õ}s and Rényi. In this theory, a network is characterized by two parameters: the size N and the link probability p. Our experimental data suggest three levels of social inclusion of friendship. The critical value pc, for which half of agents are informed, scales with the system size as N-gamma with gamma approx 0.68. Computer simulations show that the probability X varies with p as a sigmoidal curve. Influence of the correlations between neighbors is also evaluated: with increasing clustering coefficient C, X decreases.

Vectors into the Future of Mass and Interpersonal Communication Research: Big Data, Social Media, and Computational Social Science.

PubMed

Cappella, Joseph N

2017-10-01

Simultaneous developments in big data, social media, and computational social science have set the stage for how we think about and understand interpersonal and mass communication. This article explores some of the ways that these developments generate 4 hypothetical "vectors" - directions - into the next generation of communication research. These vectors include developments in network analysis, modeling interpersonal and social influence, recommendation systems, and the blurring of distinctions between interpersonal and mass audiences through narrowcasting and broadcasting. The methods and research in these arenas are occurring in areas outside the typical boundaries of the communication discipline but engage classic, substantive questions in mass and interpersonal communication.

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

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.

Functional cortical network in alpha band correlates with social bargaining.

PubMed

Billeke, Pablo; Zamorano, Francisco; Chavez, Mario; Cosmelli, Diego; Aboitiz, Francisco

2014-01-01

Solving demanding tasks requires fast and flexible coordination among different brain areas. Everyday examples of this are the social dilemmas in which goals tend to clash, requiring one to weigh alternative courses of action in limited time. In spite of this fact, there are few studies that directly address the dynamics of flexible brain network integration during social interaction. To study the preceding, we carried out EEG recordings while subjects played a repeated version of the Ultimatum Game in both human (social) and computer (non-social) conditions. We found phase synchrony (inter-site-phase-clustering) modulation in alpha band that was specific to the human condition and independent of power modulation. The strength and patterns of the inter-site-phase-clustering of the cortical networks were also modulated, and these modulations were mainly in frontal and parietal regions. Moreover, changes in the individuals' alpha network structure correlated with the risk of the offers made only in social conditions. This correlation was independent of changes in power and inter-site-phase-clustering strength. Our results indicate that, when subjects believe they are participating in a social interaction, a specific modulation of functional cortical networks in alpha band takes place, suggesting that phase synchrony of alpha oscillations could serve as a mechanism by which different brain areas flexibly interact in order to adapt ongoing behavior in socially demanding contexts.

Functional Cortical Network in Alpha Band Correlates with Social Bargaining

PubMed Central

Billeke, Pablo; Zamorano, Francisco; Chavez, Mario; Cosmelli, Diego; Aboitiz, Francisco

2014-01-01

Solving demanding tasks requires fast and flexible coordination among different brain areas. Everyday examples of this are the social dilemmas in which goals tend to clash, requiring one to weigh alternative courses of action in limited time. In spite of this fact, there are few studies that directly address the dynamics of flexible brain network integration during social interaction. To study the preceding, we carried out EEG recordings while subjects played a repeated version of the Ultimatum Game in both human (social) and computer (non-social) conditions. We found phase synchrony (inter-site-phase-clustering) modulation in alpha band that was specific to the human condition and independent of power modulation. The strength and patterns of the inter-site-phase-clustering of the cortical networks were also modulated, and these modulations were mainly in frontal and parietal regions. Moreover, changes in the individuals’ alpha network structure correlated with the risk of the offers made only in social conditions. This correlation was independent of changes in power and inter-site-phase-clustering strength. Our results indicate that, when subjects believe they are participating in a social interaction, a specific modulation of functional cortical networks in alpha band takes place, suggesting that phase synchrony of alpha oscillations could serve as a mechanism by which different brain areas flexibly interact in order to adapt ongoing behavior in socially demanding contexts. PMID:25286240

Ten Tales of Betrayal: The Threat to Corporate Infrastructure by Information Technology Insiders Analysis and Observations

DTIC Science & Technology

2005-09-01

for traditional social networks. Often the computer is used to mediate their social interactions at work. This lack of social skills tends to...substance of the case narratives. These issue areas are: Subject and Attack Characteristics, Screening, Attack Detection, Organizational and Social ...strong relationship between personal stress as well as adverse social climates and the level of risk for systems abuse in any organization that relies

Different but Similar: Computer Use Patterns between Young Korean Males and Females

ERIC Educational Resources Information Center

Lim, Keol; Meier, Ellen B.

2011-01-01

This study was developed to identify and describe new trends and gender differences in the use of computers and the Internet in South Korea. In this mixed-method study, both quantitative and qualitative techniques were used. Results indicated that both males and females used computers generally for four purposes: social networking, personal…

Network support, technology use, depression, and ART adherence among HIV-positive MSM of color.

PubMed

Holloway, I W; Tan, D; Dunlap, S L; Palmer, L; Beougher, S; Cederbaum, J A

2017-09-01

Depression is associated with poor antiretroviral therapy (ART) adherence among people living with HIV/AIDS. This relationship may be moderated by an individual's social network characteristics. Our study sought to examine social network correlates of treatment adherence among HIV-positive men recruited from social service agencies throughout Los Angeles County (N = 150) to inform technology-driven social support interventions for this population. We administered egocentric social network and computer-assisted survey interviews focused on demographic characteristics, health history, depressive symptoms, and ART adherence, where adherence was assessed by the number of reasons participants missed taking their medication, if ever. Significant univariate correlates of adherence were included in a multivariable regression analysis, where the moderating effect of having a network member who reminds participants to take their HIV medication on the relationship between depression and adherence was tested. Over 60% of participants reported clinically significant depressive symptoms; this was significantly associated with lower adherence among those without someone in their social network to remind them about taking their HIV medication, even after adjusting for covariates in an ordinary least squares regression (adjusted mean difference b = -1.61, SE = 0.42, p = 0.0003). Having a network member who reminds participants to take their ART medication significantly ameliorated the negative association between depression and treatment adherence, especially for those reporting greater depressive symptoms (p = 0.0394). Additionally, participants demonstrated high rates of technology use to communicate with social network members. In order to achieve the aims of the National HIV/AIDS Strategy, innovative interventions addressing mental health to improve ART adherence are needed. Network strategies that leverage technology may be helpful for improving ART adherence among HIV-positive men with comorbid depressive symptoms.

Computing Tutte polynomials of contact networks in classrooms

NASA Astrophysics Data System (ADS)

Hincapié, Doracelly; Ospina, Juan

2013-05-01

Objective: The topological complexity of contact networks in classrooms and the potential transmission of an infectious disease were analyzed by sex and age. Methods: The Tutte polynomials, some topological properties and the number of spanning trees were used to algebraically compute the topological complexity. Computations were made with the Maple package GraphTheory. Published data of mutually reported social contacts within a classroom taken from primary school, consisting of children in the age ranges of 4-5, 7-8 and 10-11, were used. Results: The algebraic complexity of the Tutte polynomial and the probability of disease transmission increases with age. The contact networks are not bipartite graphs, gender segregation was observed especially in younger children. Conclusion: Tutte polynomials are tools to understand the topology of the contact networks and to derive numerical indexes of such topologies. It is possible to establish relationships between the Tutte polynomial of a given contact network and the potential transmission of an infectious disease within such network

Optimizing use of library technology.

PubMed

Wink, Diane M; Killingsworth, Elizabeth K

2011-01-01

In this bimonthly series, the author examines how nurse educators can use the Internet and Web-based computer technologies such as search, communication, collaborative writing tools; social networking and social bookmarking sites; virtual worlds; and Web-based teaching and learning programs. This article describes optimizing the use of library technology.

Theoretical and Experimental Investigation of Opinion Dynamics in Small Social Networks

DTIC Science & Technology

2016-07-01

Sciences, Social Informatics and Telecommunications Engineering 2013 96 M. Gabbay described. Section 4 illustrates the application of the methodology...group of cyber terrorists has already gained access to multiple computers. The attack will attempt to disrupt and destroy a large oil refinery; at

Dynamical networks of influence in small group discussions

PubMed Central

Noriega Campero, Alejandro; Almaatouq, Abdullah

2018-01-01

In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network—a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences. PMID:29338013

Symposium Connects Government Problems with State of the Art Network Science Research

DTIC Science & Technology

2015-10-16

Symposium Connects Government Problems with State-of-the- Art Network Science Research By Rajmonda S. Caceres and Benjamin A. Miller Network...the US Gov- ernment, and match these with the state-of-the- art models and techniques developed in the network science research community. Since its... science has grown significantly in the last several years as a field at the intersec- tion of mathematics, computer science , social science , and engineering

Predicting Node Degree Centrality with the Node Prominence Profile

PubMed Central

Yang, Yang; Dong, Yuxiao; Chawla, Nitesh V.

2014-01-01

Centrality of a node measures its relative importance within a network. There are a number of applications of centrality, including inferring the influence or success of an individual in a social network, and the resulting social network dynamics. While we can compute the centrality of any node in a given network snapshot, a number of applications are also interested in knowing the potential importance of an individual in the future. However, current centrality is not necessarily an effective predictor of future centrality. While there are different measures of centrality, we focus on degree centrality in this paper. We develop a method that reconciles preferential attachment and triadic closure to capture a node's prominence profile. We show that the proposed node prominence profile method is an effective predictor of degree centrality. Notably, our analysis reveals that individuals in the early stage of evolution display a distinctive and robust signature in degree centrality trend, adequately predicted by their prominence profile. We evaluate our work across four real-world social networks. Our findings have important implications for the applications that require prediction of a node's future degree centrality, as well as the study of social network dynamics. PMID:25429797

Inhibiting diffusion of complex contagions in social networks: theoretical and experimental results

PubMed Central

Anil Kumar, V.S.; Marathe, Madhav V.; Ravi, S.S.; Rosenkrantz, Daniel J.

2014-01-01

We consider the problem of inhibiting undesirable contagions (e.g. rumors, spread of mob behavior) in social networks. Much of the work in this context has been carried out under the 1-threshold model, where diffusion occurs when a node has just one neighbor with the contagion. We study the problem of inhibiting more complex contagions in social networks where nodes may have thresholds larger than 1. The goal is to minimize the propagation of the contagion by removing a small number of nodes (called critical nodes) from the network. We study several versions of this problem and prove that, in general, they cannot even be efficiently approximated to within any factor ρ ≥ 1, unless P = NP. We develop efficient and practical heuristics for these problems and carry out an experimental study of their performance on three well known social networks, namely epinions, wikipedia and slashdot. Our results show that these heuristics perform significantly better than five other known methods. We also establish an efficiently computable upper bound on the number of nodes to which a contagion can spread and evaluate this bound on many real and synthetic networks. PMID:25750583

Bipartite graphs as models of population structures in evolutionary multiplayer games.

PubMed

Peña, Jorge; Rochat, Yannick

2012-01-01

By combining evolutionary game theory and graph theory, "games on graphs" study the evolutionary dynamics of frequency-dependent selection in population structures modeled as geographical or social networks. Networks are usually represented by means of unipartite graphs, and social interactions by two-person games such as the famous prisoner's dilemma. Unipartite graphs have also been used for modeling interactions going beyond pairwise interactions. In this paper, we argue that bipartite graphs are a better alternative to unipartite graphs for describing population structures in evolutionary multiplayer games. To illustrate this point, we make use of bipartite graphs to investigate, by means of computer simulations, the evolution of cooperation under the conventional and the distributed N-person prisoner's dilemma. We show that several implicit assumptions arising from the standard approach based on unipartite graphs (such as the definition of replacement neighborhoods, the intertwining of individual and group diversity, and the large overlap of interaction neighborhoods) can have a large impact on the resulting evolutionary dynamics. Our work provides a clear example of the importance of construction procedures in games on graphs, of the suitability of bigraphs and hypergraphs for computational modeling, and of the importance of concepts from social network analysis such as centrality, centralization and bipartite clustering for the understanding of dynamical processes occurring on networked population structures.

Outline of CS application experiments

NASA Astrophysics Data System (ADS)

Otsu, Y.; Kondoh, K.; Matsumoto, M.

1985-09-01

To promote and investigate the practical application of satellite use, CS application experiments for various social activity needs, including those of public services such as the National Police Agency and the Japanese National Railway, computer network services, news material transmissions, and advanced teleconference activities, were performed. Public service satellite communications systems were developed and tested. Based on results obtained, several public services have implemented CS-2 for practical disaster-back-up uses. Practical application computer network and enhanced video-conference experiments have also been performed.

Gender differences in sexual risk and sexually transmitted infections correlate with gender differences in social networks among San Francisco homeless youth.

PubMed

Valente, Annie M; Auerswald, Colette L

2013-10-01

To explore whether gender differences in sexual risk and sexually transmitted infections (STIs) among homeless youth may be explained in part by gender differences in their social networks. Our sample includes 258 youth (64% male) recruited in San Francisco from street venues and transitional programs. Participants completed an audio computer-administered self-interview survey regarding their housing status and risk behaviors and an interviewer-administered survey regarding their social networks, and were tested for STIs (chlamydia and gonorrhea). We examined relationships between sexual risk and STI rates and social network characteristics by gender. Condom use was lower in young women than in young men, whereas young women were more likely to have an injection drug user (IDU) sex partner and to be diagnosed with an STI. Homeless young men were more likely to have stably housed contacts and same-sex friendships in their social networks than were young women. Stably housed network contacts were associated with increased condom use and decreased STI prevalence in young men. Same-sex friends were associated with increased condom use in young women. No young woman with a family member in her network had an IDU sex partner. Having a network member who had been recently incarcerated was associated with having an IDU sex partner for young women. Homeless young women's networks may place them at greater risk for STIs than young men. Increasing mainstream contacts and same-gender friendships may protect all homeless youth from STIs. Interventions addressing homeless young women's social networks may decrease their gender-disparate STI risk. Copyright © 2013 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Trends in Social Science: The Impact of Computational and Simulative Models

NASA Astrophysics Data System (ADS)

Conte, Rosaria; Paolucci, Mario; Cecconi, Federico

This paper discusses current progress in the computational social sciences. Specifically, it examines the following questions: Are the computational social sciences exhibiting positive or negative developments? What are the roles of agent-based models and simulation (ABM), network analysis, and other "computational" methods within this dynamic? (Conte, The necessity of intelligent agents in social simulation, Advances in Complex Systems, 3(01n04), 19-38, 2000; Conte 2010; Macy, Annual Review of Sociology, 143-166, 2002). Are there objective indicators of scientific growth that can be applied to different scientific areas, allowing for comparison among them? In this paper, some answers to these questions are presented and discussed. In particular, comparisons among different disciplines in the social and computational sciences are shown, taking into account their respective growth trends in the number of publication citations over the last few decades (culled from Google Scholar). After a short discussion of the methodology adopted, results of keyword-based queries are presented, unveiling some unexpected local impacts of simulation on the takeoff of traditionally poorly productive disciplines.

A pilot test of a motivational interviewing social network intervention to reduce substance use among housing first residents.

PubMed

Kennedy, David P; Osilla, Karen Chan; Hunter, Sarah B; Golinelli, Daniela; Maksabedian Hernandez, Ervant; Tucker, Joan S

2018-03-01

This article presents findings of a pilot test of a Motivational Interviewing social network intervention (MI-SNI) to enhance motivation to reduce high risk alcohol and other drug (AOD) use among formerly homeless individuals transitioning to housing. Delivered in-person by a facilitator trained in MI, this four-session computer-assisted intervention provides personalized social network visualization feedback to help participants understand the people in their network who trigger their alcohol and other drug (AOD) use and those who support abstinence. If ready, participants are encouraged to make changes to their social network to help reduce their own high-risk behavior. Participants were 41 individuals (33 male, 7 female, 1 other; 23 African-American, 5 non-Latino White, 6 Latino, 7 other, mean age 48) who were transitioning from homelessness to permanent supportive housing. They were randomly assigned to either the MI-SNI condition or usual care. Readiness to change AOD use, AOD abstinence self-efficacy, and AOD use were assessed at baseline and shortly after the final intervention session for the MI-SNI arm and around 3-months after baseline for the control arm. Acceptability of the intervention was also evaluated. MI-SNI participants reported increased readiness to change AOD use compared to control participants. We also conducted a subsample analysis for participants at one housing program and found a significant intervention effect on readiness to change AOD use, AOD abstinence self-efficacy, and alcohol use compared to control participants. Participants rated the intervention as highly acceptable. We conclude that a brief computer-assisted Motivational Interviewing social network intervention has potential to efficaciously impact readiness to change AOD use, AOD abstinence self-efficacy, and AOD use among formerly homeless individuals transitioning to permanent supportive housing, and warrants future study in larger clinical trials. Copyright © 2017 Elsevier Inc. All rights reserved.

Clinician attitudes, social norms and intentions to use a computer-assisted intervention.

PubMed

Buti, Allison L; Eakins, Danielle; Fussell, Holly; Kunkel, Lynn E; Kudura, Aisha; McCarty, Dennis

2013-04-01

The National Drug Abuse Treatment Clinical Trials Network (CTN) works to bridge the gap between research and practice and tested a Web-delivered psychosocial intervention (the Therapeutic Education System, TES) in 10 community treatment centers. Computer-assisted therapies, such as Web-delivered interventions, may improve the consistency and efficiency of treatment for alcohol and drug use disorders. Prior to the start of the study, we surveyed counselors (N=96) in participating treatment centers and assessed counselor attitudes, perceived social norms and intentions to use a Web-delivered intervention. Analysis of the intention to adopt a Web-delivered intervention assessed the influence of attitudes and perceived social norms. Perceived social norms were a significant contributor to clinician intention to adopt Web-based interventions while attitude was not. To promote successful implementation, it may be helpful to create social norms supportive of computer-assisted therapies. Copyright © 2013 Elsevier Inc. All rights reserved.

GeoChronos: An On-line Collaborative Platform for Earth Observation Scientists

NASA Astrophysics Data System (ADS)

Gamon, J. A.; Kiddle, C.; Curry, R.; Markatchev, N.; Zonta-Pastorello, G., Jr.; Rivard, B.; Sanchez-Azofeifa, G. A.; Simmonds, R.; Tan, T.

2009-12-01

Recent advances in cyberinfrastructure are offering new solutions to the growing challenges of managing and sharing large data volumes. Web 2.0 and social networking technologies, provide the means for scientists to collaborate and share information more effectively. Cloud computing technologies can provide scientists with transparent and on-demand access to applications served over the Internet in a dynamic and scalable manner. Semantic Web technologies allow for data to be linked together in a manner understandable by machines, enabling greater automation. Combining all of these technologies together can enable the creation of very powerful platforms. GeoChronos (http://geochronos.org/), part of a CANARIE Network Enabled Platforms project, is an online collaborative platform that incorporates these technologies to enable members of the earth observation science community to share data and scientific applications and to collaborate more effectively. The GeoChronos portal is built on an open source social networking platform called Elgg. Elgg provides a full set of social networking functionalities similar to Facebook including blogs, tags, media/document sharing, wikis, friends/contacts, groups, discussions, message boards, calendars, status, activity feeds and more. An underlying cloud computing infrastructure enables scientists to access dynamically provisioned applications via the portal for visualizing and analyzing data. Users are able to access and run the applications from any computer that has a Web browser and Internet connectivity and do not need to manage and maintain the applications themselves. Semantic Web Technologies, such as the Resource Description Framework (RDF) are being employed for relating and linking together spectral, satellite, meteorological and other data. Social networking functionality plays an integral part in facilitating the sharing of data and applications. Examples of recent GeoChronos users during the early testing phase have included the IAI International Wireless Sensor Networking Summer School at the University of Alberta, and the IAI Tropi-Dry community. Current GeoChronos activities include the development of a web-based spectral library and related analytical and visualization tools, in collaboration with members of the SpecNet community. The GeoChronos portal will be open to all members of the earth observation science community when the project nears completion at the end of 2010.

Reduced Social Network Drinking is Associated with Improved Response Inhibition in Women During Early Recovery from Alcohol Use Disorders: A Pilot Study.

PubMed

McCutcheon, Vivia V; Luke, Douglas A; Lessov-Schlaggar, Christina N

2016-01-01

Social support for recovery from alcohol use disorders (AUDs) is associated with improvements in self-reported impulsive behavior in individuals treated for AUDs. We build on these findings using a behavioral task-based measure of response inhibition, a well-defined component of impulsivity, to examine the association of disinhibition with alcohol-specific social network characteristics during early recovery. Women (n = 28) were recruited from treatment for AUD within 3 to 4 weeks of their last drink and were assessed at baseline and again 3 months later. Outcome measures were level of disinhibition at baseline and change in disinhibition from baseline to follow-up, measured using a computer-based continuous performance test. The primary independent variables were level of drinking in the social network at baseline and change in network drinking from baseline to follow-up. The sample [50% black, age M (SD) = 42.3 (9.5)] reported high rates of physical and sexual abuse before age 13 (43%), psychiatric disorder (71%), drug use disorder (78%), and previous treatment (71%). More drinking in participants' social networks was associated with greater disinhibition at baseline (β = 12.5, 95% CI = 6.3, 18.7). A reduction in network drinking from baseline to follow-up was associated with reduced disinhibition (β = -6.0, 95% CI = -11.3, -0.78) independent of IQ, recent alcohol consumption, and self-reported negative urgency. This study extends previous findings of an association between social networks and self-reported impulsivity to a neurobehavioral phenotype, response inhibition, suggesting that abstinence-supporting social networks may play a role in cognitive change during early recovery from AUDs. Copyright © 2015 by the Research Society on Alcoholism.

Cooperation prevails when individuals adjust their social ties.

PubMed

Santos, Francisco C; Pacheco, Jorge M; Lenaerts, Tom

2006-10-20

Conventional evolutionary game theory predicts that natural selection favours the selfish and strong even though cooperative interactions thrive at all levels of organization in living systems. Recent investigations demonstrated that a limiting factor for the evolution of cooperative interactions is the way in which they are organized, cooperators becoming evolutionarily competitive whenever individuals are constrained to interact with few others along the edges of networks with low average connectivity. Despite this insight, the conundrum of cooperation remains since recent empirical data shows that real networks exhibit typically high average connectivity and associated single-to-broad-scale heterogeneity. Here, a computational model is constructed in which individuals are able to self-organize both their strategy and their social ties throughout evolution, based exclusively on their self-interest. We show that the entangled evolution of individual strategy and network structure constitutes a key mechanism for the sustainability of cooperation in social networks. For a given average connectivity of the population, there is a critical value for the ratio W between the time scales associated with the evolution of strategy and of structure above which cooperators wipe out defectors. Moreover, the emerging social networks exhibit an overall heterogeneity that accounts very well for the diversity of patterns recently found in acquired data on social networks. Finally, heterogeneity is found to become maximal when W reaches its critical value. These results show that simple topological dynamics reflecting the individual capacity for self-organization of social ties can produce realistic networks of high average connectivity with associated single-to-broad-scale heterogeneity. On the other hand, they show that cooperation cannot evolve as a result of "social viscosity" alone in heterogeneous networks with high average connectivity, requiring the additional mechanism of topological co-evolution to ensure the survival of cooperative behaviour.

Electronic Advocacy and Social Welfare Policy Education

ERIC Educational Resources Information Center

Moon, Sung Seek; DeWeaver, Kevin L.

2005-01-01

The rapid increase in the number of low-cost computers, the proliferation of user-friendly software, and the development of electronic networks have created the "informatics era." The Internet is a rapidly growing communication resource that is becoming mainstream in the American society. Computer-based electronic political advocacy by social…

The Impact of Electronic Media on Faculty Evaluation

ERIC Educational Resources Information Center

Barkhi, Reza; Williams, Paul

2010-01-01

With the proliferation of computer networks and the increased use of Internet-based applications, many forms of social interactions now take place in an on-line context through "Computer-Mediated Communication" (CMC). Many universities are now reaping the benefits of using CMC applications to collect data on student evaluations of…

Language Use in Asynchronous Computer-Mediated Communication in Taiwan

ERIC Educational Resources Information Center

Huang, Daphne Li-jung

2009-01-01

This paper describes how Chinese-English bilinguals in Taiwan use their languages in asynchronous computer-mediated communication, specifically, via Bulletin Board System (BBS) and email. The main data includes two types: emails collected from a social network and postings collected from two BBS websites. By examining patterns of language choice…

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.

Mentalizing and motivation neural function during social interactions in autism spectrum disorders☆

PubMed Central

Assaf, Michal; Hyatt, Christopher J.; Wong, Christina G.; Johnson, Matthew R.; Schultz, Robert T.; Hendler, Talma; Pearlson, Godfrey D.

2013-01-01

Autism Spectrum Disorders (ASDs) are characterized by core deficits in social functions. Two theories have been suggested to explain these deficits: mind-blindness theory posits impaired mentalizing processes (i.e. decreased ability for establishing a representation of others' state of mind), while social motivation theory proposes that diminished reward value for social information leads to reduced social attention, social interactions, and social learning. Mentalizing and motivation are integral to typical social interactions, and neuroimaging evidence points to independent brain networks that support these processes in healthy individuals. However, the simultaneous function of these networks has not been explored in individuals with ASDs. We used a social, interactive fMRI task, the Domino game, to explore mentalizing- and motivation-related brain activation during a well-defined interval where participants respond to rewards or punishments (i.e. motivation) and concurrently process information about their opponent's potential next actions (i.e. mentalizing). Thirteen individuals with high-functioning ASDs, ages 12–24, and 14 healthy controls played fMRI Domino games against a computer-opponent and separately, what they were led to believe was a human-opponent. Results showed that while individuals with ASDs understood the game rules and played similarly to controls, they showed diminished neural activity during the human-opponent runs only (i.e. in a social context) in bilateral middle temporal gyrus (MTG) during mentalizing and right Nucleus Accumbens (NAcc) during reward-related motivation (Pcluster < 0.05 FWE). Importantly, deficits were not observed in these areas when playing against a computer-opponent or in areas related to motor and visual processes. These results demonstrate that while MTG and NAcc, which are critical structures in the mentalizing and motivation networks, respectively, activate normally in a non-social context, they fail to respond in an otherwise identical social context in ASD compared to controls. We discuss implications to both the mind-blindness and social motivation theories of ASD and the importance of social context in research and treatment protocols. PMID:24273716

Biological mechanisms beyond network analysis via mathematical modeling. Comment on "Network science of biological systems at different scales: A review" by Marko Gosak et al.

NASA Astrophysics Data System (ADS)

Pedersen, Morten Gram

2018-03-01

Methods from network theory are increasingly used in research spanning from engineering and computer science to psychology and the social sciences. In this issue, Gosak et al. [1] provide a thorough review of network science applications to biological systems ranging from the subcellular world via neuroscience to ecosystems, with special attention to the insulin-secreting beta-cells in pancreatic islets.

The Intellectual Structure of Metacognitive Scaffolding in Science Education: A Co-Citation Network Analysis

ERIC Educational Resources Information Center

Tang, Kai-Yu; Wang, Chia-Yu; Chang, Hsin-Yi; Chen, Sufen; Lo, Hao-Chang; Tsai, Chin-Chung

2016-01-01

The issues of metacognitive scaffolding in science education (MSiSE) have become increasingly popular and important. Differing from previous content reviews, this study proposes a series of quantitative computer-based analyses by integrating document co-citation analysis, social network analysis, and exploratory factor analysis to explore the…

Quantum neural networks: Current status and prospects for development

NASA Astrophysics Data System (ADS)

Altaisky, M. V.; Kaputkina, N. E.; Krylov, V. A.

2014-11-01

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.

The influence of phylogeny, social style, and sociodemographic factors on macaque social network structure.

PubMed

Balasubramaniam, Krishna N; Beisner, Brianne A; Berman, Carol M; De Marco, Arianna; Duboscq, Julie; Koirala, Sabina; Majolo, Bonaventura; MacIntosh, Andrew J; McFarland, Richard; Molesti, Sandra; Ogawa, Hideshi; Petit, Odile; Schino, Gabriele; Sosa, Sebastian; Sueur, Cédric; Thierry, Bernard; de Waal, Frans B M; McCowan, Brenda

2018-01-01

Among nonhuman primates, the evolutionary underpinnings of variation in social structure remain debated, with both ancestral relationships and adaptation to current conditions hypothesized to play determining roles. Here we assess whether interspecific variation in higher-order aspects of female macaque (genus: Macaca) dominance and grooming social structure show phylogenetic signals, that is, greater similarity among more closely-related species. We use a social network approach to describe higher-order characteristics of social structure, based on both direct interactions and secondary pathways that connect group members. We also ask whether network traits covary with each other, with species-typical social style grades, and/or with sociodemographic characteristics, specifically group size, sex-ratio, and current living condition (captive vs. free-living). We assembled 34-38 datasets of female-female dyadic aggression and allogrooming among captive and free-living macaques representing 10 species. We calculated dominance (transitivity, certainty), and grooming (centrality coefficient, Newman's modularity, clustering coefficient) network traits as aspects of social structure. Computations of K statistics and randomization tests on multiple phylogenies revealed moderate-strong phylogenetic signals in dominance traits, but moderate-weak signals in grooming traits. GLMMs showed that grooming traits did not covary with dominance traits and/or social style grade. Rather, modularity and clustering coefficient, but not centrality coefficient, were strongly predicted by group size and current living condition. Specifically, larger groups showed more modular networks with sparsely-connected clusters than smaller groups. Further, this effect was independent of variation in living condition, and/or sampling effort. In summary, our results reveal that female dominance networks were more phylogenetically conserved across macaque species than grooming networks, which were more labile to sociodemographic factors. Such findings narrow down the processes that influence interspecific variation in two core aspects of macaque social structure. Future directions should include using phylogeographic approaches, and addressing challenges in examining the effects of socioecological factors on primate social structure. © 2017 Wiley Periodicals, Inc.

Information diffusion, Facebook clusters, and the simplicial model of social aggregation: a computational simulation of simplicial diffusers for community health interventions.

PubMed

Kee, Kerk F; Sparks, Lisa; Struppa, Daniele C; Mannucci, Mirco A; Damiano, Alberto

2016-01-01

By integrating the simplicial model of social aggregation with existing research on opinion leadership and diffusion networks, this article introduces the constructs of simplicial diffusers (mathematically defined as nodes embedded in simplexes; a simplex is a socially bonded cluster) and simplicial diffusing sets (mathematically defined as minimal covers of a simplicial complex; a simplicial complex is a social aggregation in which socially bonded clusters are embedded) to propose a strategic approach for information diffusion of cancer screenings as a health intervention on Facebook for community cancer prevention and control. This approach is novel in its incorporation of interpersonally bonded clusters, culturally distinct subgroups, and different united social entities that coexist within a larger community into a computational simulation to select sets of simplicial diffusers with the highest degree of information diffusion for health intervention dissemination. The unique contributions of the article also include seven propositions and five algorithmic steps for computationally modeling the simplicial model with Facebook data.

Combining Computational and Social Effort for Collaborative Problem Solving

PubMed Central

Wagy, Mark D.; Bongard, Josh C.

2015-01-01

Rather than replacing human labor, there is growing evidence that networked computers create opportunities for collaborations of people and algorithms to solve problems beyond either of them. In this study, we demonstrate the conditions under which such synergy can arise. We show that, for a design task, three elements are sufficient: humans apply intuitions to the problem, algorithms automatically determine and report back on the quality of designs, and humans observe and innovate on others’ designs to focus creative and computational effort on good designs. This study suggests how such collaborations should be composed for other domains, as well as how social and computational dynamics mutually influence one another during collaborative problem solving. PMID:26544199

A social network analysis of substance use among immigrant adolescents in six European cities.

PubMed

Lorant, Vincent; Soto Rojas, Victoria; Bécares, Laia; Kinnunen, Jaana M; Kuipers, Mirte A G; Moor, Irene; Roscillo, Gaetano; Alves, Joana; Grard, Adeline; Rimpelä, Arja; Federico, Bruno; Richter, Matthias; Perelman, Julian; Kunst, Anton E

2016-11-01

Social integration and the health of adolescents with a migration background is a major concern in multicultural societies. The literature, however, has paid little attention to the wider determinants of their health behaviours, including the composition of their social networks. The aim of this study was to describe the composition of adolescents' social networks according to migration background, and to examine how social networks are associated with substance use. In 2013, the SILNE study surveyed 11,015 secondary-school adolescents in 50 schools in six European cities in Belgium, Finland, Germany, Italy, the Netherlands, and Portugal, using a social network design. Each adolescent nominated up to five of their best and closest friends. Migration status was defined as first-generation migrants, second-generation migrants, and speaking another language at home. We computed two groups of network structural positions, the centrality of individual adolescents in networks, and the homophily of their social ties regarding migration (same-migration). Multilevel logistic regression was used to model the association between network structural position and smoking, alcohol use, and cannabis use. Compared with non-migrant adolescents, adolescents with migration backgrounds had similar relationship patterns. But almost half their social ties were with same-migration-background adolescents; non-migrants had few social ties to migrants. For adolescents with a migration background, a higher proportion of social ties with non-migrants was associated with increased use of cannabis (OR = 1.07, p = 0.03) and alcohol (OR = 1.08, p < 0.01), but not with increased smoking (p = 0.60). Popular migrant adolescents were at less risk of smoking, alcohol use, and cannabis use than popular non-migrant adolescents. Homophily of social ties by migration background is noticeable in European schools. The tendency of migrant adolescents to have same-migration social ties may isolate them from non-migrant adolescents, but also reduces their risky health behaviours, in particular cannabis and alcohol use. Copyright © 2016 Elsevier Ltd. All rights reserved.

Women's Social Networking: Steps that Organize Low-Technology Users with Emergency Needs

ERIC Educational Resources Information Center

Gary, Jon O.

2011-01-01

The purpose of the study was for the researcher to develop and have evaluated the content, organization and ease of use of a computer-training program "Emergency Response Quick Start" book based on Information and Communication Technology environmental designs. This book was most notably based on underutilized women social networking…

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.

Developing Online Learning Resources: Big Data, Social Networks, and Cloud Computing to Support Pervasive Knowledge

ERIC Educational Resources Information Center

Anshari, Muhammad; Alas, Yabit; Guan, Lim Sei

2016-01-01

Utilizing online learning resources (OLR) from multi channels in learning activities promise extended benefits from traditional based learning-centred to a collaborative based learning-centred that emphasises pervasive learning anywhere and anytime. While compiling big data, cloud computing, and semantic web into OLR offer a broader spectrum of…

Using a Cloud-Based Computing Environment to Support Teacher Training on Common Core Implementation

ERIC Educational Resources Information Center

Robertson, Cory

2013-01-01

A cloud-based computing environment, Google Apps for Education (GAFE), has provided the Anaheim City School District (ACSD) a comprehensive and collaborative avenue for creating, sharing, and editing documents, calendars, and social networking communities. With this environment, teachers and district staff at ACSD are able to utilize the deep…

Law School Experience in Pervasive Electronic Communications.

ERIC Educational Resources Information Center

Shiels, Rosemary

1994-01-01

Installation of a schoolwide local area computer network at Chicago-Kent College of Law (Illinois) is described. Uses of electronic mail within a course on computer law are described. Additional social, administrative, and research uses of electronic mail are noted as are positive effects and emerging problems (e.g., burdens on recipients and…

Characteristics of the Navy Laboratory Warfare Center Technical Workforce

DTIC Science & Technology

2013-09-29

Mathematics and Information Science (M&IS) Actuarial Science 1510 Computer Science 1550 Gen. Math & Statistics 1501 Mathematics 1520 Operations...Admin. Network Systems & Data Communication Analysts Actuaries Mathematicians Operations Research Analyst Statisticians Social Science (SS...workforce was sub-divided into six broad occupational groups: Life Science , Physical Science , Engineering, Mathematics, Computer Science and Information

Refocusing the Vision: The Future of Instructional Technology

ERIC Educational Resources Information Center

Pence, Harry E.; McIntosh, Steven

2011-01-01

Two decades ago, many campuses mobilized a major effort to deal with a clear problem; faculty and students needed access to desktop computing technologies. Now the situation is much more complex. Responding to the current challenges, like mobile computing and social networking, will be ore difficult but equally important. There is a clear need for…

The Teaching Voice on the Learning Platform: Seeking Classroom Climates within a Virtual Learning Environment

ERIC Educational Resources Information Center

Crook, Charles; Cluley, Robert

2009-01-01

University staff are now encouraged to supplement their classroom activity with computer-based tools and resources accessible through virtual learning environments (VLEs). Meanwhile, university students increasingly make recreational use of computer networks in the form of various social software applications. This paper explores tensions of…

A Living Library: New Model for Global Electronic Interactivity and Networking in the Garden.

ERIC Educational Resources Information Center

Sherk, Bonnie

1995-01-01

Describes the Living Library, an idea to create a network of international cultural parks in different cities of the world using new communications technologies on-line in a garden setting, bringing the humanities, sciences, and social sciences to life through plants, visual and performed artworks, lectures, and computer and on-line satellite…

Information dynamics algorithm for detecting communities in networks

NASA Astrophysics Data System (ADS)

Massaro, Emanuele; Bagnoli, Franco; Guazzini, Andrea; Lió, Pietro

2012-11-01

The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method [4] by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.

Developing an intelligence analysis process through social network analysis

NASA Astrophysics Data System (ADS)

Waskiewicz, Todd; LaMonica, Peter

2008-04-01

Intelligence analysts are tasked with making sense of enormous amounts of data and gaining an awareness of a situation that can be acted upon. This process can be extremely difficult and time consuming. Trying to differentiate between important pieces of information and extraneous data only complicates the problem. When dealing with data containing entities and relationships, social network analysis (SNA) techniques can be employed to make this job easier. Applying network measures to social network graphs can identify the most significant nodes (entities) and edges (relationships) and help the analyst further focus on key areas of concern. Strange developed a model that identifies high value targets such as centers of gravity and critical vulnerabilities. SNA lends itself to the discovery of these high value targets and the Air Force Research Laboratory (AFRL) has investigated several network measures such as centrality, betweenness, and grouping to identify centers of gravity and critical vulnerabilities. Using these network measures, a process for the intelligence analyst has been developed to aid analysts in identifying points of tactical emphasis. Organizational Risk Analyzer (ORA) and Terrorist Modus Operandi Discovery System (TMODS) are the two applications used to compute the network measures and identify the points to be acted upon. Therefore, the result of leveraging social network analysis techniques and applications will provide the analyst and the intelligence community with more focused and concentrated analysis results allowing them to more easily exploit key attributes of a network, thus saving time, money, and manpower.

Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games

PubMed Central

Peña, Jorge; Rochat, Yannick

2012-01-01

By combining evolutionary game theory and graph theory, “games on graphs” study the evolutionary dynamics of frequency-dependent selection in population structures modeled as geographical or social networks. Networks are usually represented by means of unipartite graphs, and social interactions by two-person games such as the famous prisoner’s dilemma. Unipartite graphs have also been used for modeling interactions going beyond pairwise interactions. In this paper, we argue that bipartite graphs are a better alternative to unipartite graphs for describing population structures in evolutionary multiplayer games. To illustrate this point, we make use of bipartite graphs to investigate, by means of computer simulations, the evolution of cooperation under the conventional and the distributed N-person prisoner’s dilemma. We show that several implicit assumptions arising from the standard approach based on unipartite graphs (such as the definition of replacement neighborhoods, the intertwining of individual and group diversity, and the large overlap of interaction neighborhoods) can have a large impact on the resulting evolutionary dynamics. Our work provides a clear example of the importance of construction procedures in games on graphs, of the suitability of bigraphs and hypergraphs for computational modeling, and of the importance of concepts from social network analysis such as centrality, centralization and bipartite clustering for the understanding of dynamical processes occurring on networked population structures. PMID:22970237

Synchronization in complex networks

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

Arenas, A.; Diaz-Guilera, A.; Moreno, Y.

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analyticalmore » approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.« less

Bridging online and offline social networks: Multiplex analysis

NASA Astrophysics Data System (ADS)

Filiposka, Sonja; Gajduk, Andrej; Dimitrova, Tamara; Kocarev, Ljupco

2017-04-01

We show that three basic actor characteristics, namely normalized reciprocity, three cycles, and triplets, can be expressed using an unified framework that is based on computing the similarity index between two sets associated with the actor: the set of her/his friends and the set of those considering her/him as a friend. These metrics are extended to multiplex networks and then computed for two friendship networks generated by collecting data from two groups of undergraduate students. We found that in offline communication strong and weak ties are (almost) equally presented, while in online communication weak ties are dominant. Moreover, weak ties are much less reciprocal than strong ties. However, across different layers of the multiplex network reciprocities are preserved, while triads (measured with normalized three cycles and triplets) are not significant.

Teaching an Interdisciplinary Graduate-Level Methods Course in an Openly-Networked Connected Learning Environment: A Glass Half-Full

ERIC Educational Resources Information Center

Secret, Mary; Bryant, Nita L.; Cummings, Cory R.

2017-01-01

Our paper describes the design and delivery of an online interdisciplinary social science research methods course (ISRM) for graduate students in sociology, education, social work, and public administration. Collaborative activities and learning took place in two types of computer-mediated learning environments: a closed Blackboard course…

Tapping the Educational Potential of Facebook: Guidelines for Use in Higher Education

ERIC Educational Resources Information Center

Wang, Rex; Scown, Phil; Urquhart, Cathy; Hardman, Julie

2014-01-01

Facebook is a frequently used Computer Mediated Environment (CME) for students and others to build social connections, with identities and deposited self-expression. Its widespread use makes it appropriate for consideration as an educational tool; though one that does not yet have clear guidelines for use. Whether a social networking site can be…

Exploring Students' E-Learning Effectiveness through the Use of Line Chat Application

ERIC Educational Resources Information Center

Limsuthiwanpoom, Tassaneenart; Kanthawongs, Penjira; Kanthawongs, Penjuree; Suwandee, Sasithorn

2016-01-01

This study explores the impact of motivational goals for using social networking sites (SNS) usage and computer self-efficacy towards e-learning effectiveness of the 155 students from different schools at a private university, in Pathum Thani province, Thailand during April to May of academic year 2015/2016. Social dimension and human interaction…

Let's chat: developmental neural bases of social motivation during real-time peer interaction.

PubMed

Warnell, Katherine Rice; Sadikova, Eleonora; Redcay, Elizabeth

2018-05-01

Humans are motivated to interact with each other, but the neural bases of social motivation have been predominantly examined in non-interactive contexts. Understanding real-world social motivation is of special importance during middle childhood (ages 8-12), a period when social skills improve, social networks grow, and social brain networks specialize. To assess interactive social motivation, the current study used a novel fMRI paradigm in which children believed they were chatting with a peer. The design targeted two phases of interaction: (1) Initiation, in which children engaged in a social bid via sharing a like or hobby, and (2) Reply, in which children received either an engaged ("Me too") or non-engaged ("I'm away") reply from the peer. On control trials, children were told that their answers were not shared and that they would receive either engaged ("Matched") or non-engaged ("Disconnected") replies from the computer. Results indicated that during Initiation and Reply, key components of reward circuitry (e.g., ventral striatum) were more active for the peer than the computer trials. In addition, during Reply, social cognitive regions were more activated by the peer, and this social cognitive specialization increased with age. Finally, the effect of engagement type on reward circuitry activation was larger for social than non-social trials, indicating developmental sensitivity to social contingency. These findings demonstrate that both reward and social cognitive brain systems support real-time social interaction in middle childhood. An interactive approach to understanding social reward has implications for clinical disorders, where social motivation is more affected in real-world contexts. © 2017 John Wiley & Sons Ltd.

Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information.

PubMed

Jang, Haeran; An, Ji-Young

2014-07-01

Utilizing social network analysis, this study aimed to analyze the main keywords in the literature regarding the health literacy of and the use of online health information by aged persons over 65. Medical Subject Heading keywords were extracted from articles on the PubMed database of the National Library of Medicine. For health literacy, 110 articles out of 361 were initially extracted. Seventy-one keywords out of 1,021 were finally selected after removing repeated keywords and applying pruning. Regarding the use of online health information, 19 articles out of 26 were selected. One hundred forty-four keywords were initially extracted. After removing the repeated keywords, 74 keywords were finally selected. Health literacy was found to be strongly connected with 'Health knowledge, attitudes, practices' and 'Patient education as topic.' 'Computer literacy' had strong connections with 'Internet' and 'Attitude towards computers.' 'Computer literacy' was connected to 'Health literacy,' and was studied according to the parameters 'Attitude towards health' and 'Patient education as topic.' The use of online health information was strongly connected with 'Health knowledge, attitudes, practices,' 'Consumer health information,' 'Patient education as topic,' etc. In the network, 'Computer literacy' was connected with 'Health education,' 'Patient satisfaction,' 'Self-efficacy,' 'Attitude to computer,' etc. Research on older citizens' health literacy and their use of online health information was conducted together with study of computer literacy, patient education, attitude towards health, health education, patient satisfaction, etc. In particular, self-efficacy was noted as an important keyword. Further research should be conducted to identify the effective outcomes of self-efficacy in the area of interest.

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

Influencing Busy People in a Social Network

PubMed Central

Sarkar, Kaushik; Sundaram, Hari

2016-01-01

We identify influential early adopters in a social network, where individuals are resource constrained, to maximize the spread of multiple, costly behaviors. A solution to this problem is especially important for viral marketing. The problem of maximizing influence in a social network is challenging since it is computationally intractable. We make three contributions. First, we propose a new model of collective behavior that incorporates individual intent, knowledge of neighbors actions and resource constraints. Second, we show that the multiple behavior influence maximization is NP-hard. Furthermore, we show that the problem is submodular, implying the existence of a greedy solution that approximates the optimal solution to within a constant. However, since the greedy algorithm is expensive for large networks, we propose efficient heuristics to identify the influential individuals, including heuristics to assign behaviors to the different early adopters. We test our approach on synthetic and real-world topologies with excellent results. We evaluate the effectiveness under three metrics: unique number of participants, total number of active behaviors and network resource utilization. Our heuristics produce 15-51% increase in expected resource utilization over the naïve approach. PMID:27711127

Influencing Busy People in a Social Network.

PubMed

Sarkar, Kaushik; Sundaram, Hari

2016-01-01

We identify influential early adopters in a social network, where individuals are resource constrained, to maximize the spread of multiple, costly behaviors. A solution to this problem is especially important for viral marketing. The problem of maximizing influence in a social network is challenging since it is computationally intractable. We make three contributions. First, we propose a new model of collective behavior that incorporates individual intent, knowledge of neighbors actions and resource constraints. Second, we show that the multiple behavior influence maximization is NP-hard. Furthermore, we show that the problem is submodular, implying the existence of a greedy solution that approximates the optimal solution to within a constant. However, since the greedy algorithm is expensive for large networks, we propose efficient heuristics to identify the influential individuals, including heuristics to assign behaviors to the different early adopters. We test our approach on synthetic and real-world topologies with excellent results. We evaluate the effectiveness under three metrics: unique number of participants, total number of active behaviors and network resource utilization. Our heuristics produce 15-51% increase in expected resource utilization over the naïve approach.

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.

75 FR 9548 - Reduction of Use of Social Security Numbers (SSN) in the Department of Defense (DoD)

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-03

... authorization to receive specific categories of information. Computer network. The constituent element of an... elements reside on a computer. Form. A fixed arrangement of captioned spaces designed for entering and... activities of the Government or because of the information value of the data in them. Survey. An instrument...

Offdiagonal complexity: A computationally quick complexity measure for graphs and networks

NASA Astrophysics Data System (ADS)

Claussen, Jens Christian

2007-02-01

A vast variety of biological, social, and economical networks shows topologies drastically differing from random graphs; yet the quantitative characterization remains unsatisfactory from a conceptual point of view. Motivated from the discussion of small scale-free networks, a biased link distribution entropy is defined, which takes an extremum for a power-law distribution. This approach is extended to the node-node link cross-distribution, whose nondiagonal elements characterize the graph structure beyond link distribution, cluster coefficient and average path length. From here a simple (and computationally cheap) complexity measure can be defined. This offdiagonal complexity (OdC) is proposed as a novel measure to characterize the complexity of an undirected graph, or network. While both for regular lattices and fully connected networks OdC is zero, it takes a moderately low value for a random graph and shows high values for apparently complex structures as scale-free networks and hierarchical trees. The OdC approach is applied to the Helicobacter pylori protein interaction network and randomly rewired surrogates.

PuLP/XtraPuLP : Partitioning Tools for Extreme-Scale Graphs

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

Slota, George M; Rajamanickam, Sivasankaran; Madduri, Kamesh

2017-09-21

PuLP/XtraPulp is software for partitioning graphs from several real-world problems. Graphs occur in several places in real world from road networks, social networks and scientific simulations. For efficient parallel processing these graphs have to be partitioned (split) with respect to metrics such as computation and communication costs. Our software allows such partitioning for massive graphs.

How Does Information Spread on Social Media Lead to Effective Change?

PubMed

Varghese, Thomas K

2017-09-01

Social media encompasses computer-mediated technologies that facilitate the creation and sharing of information, ideas, and other forms of expression via virtual communities and networks. The key, of course, is the ability and willingness for information to be shared. But why does some information spread on social media and not others? What factors translate social media awareness to action? In this article, we explore these themes using case studies, as well as tips on how you can utilize social media to effectively champion a cause.

Using Social Network Analysis to Assess Mentorship and Collaboration in a Public Health Network.

PubMed

Petrescu-Prahova, Miruna; Belza, Basia; Leith, Katherine; Allen, Peg; Coe, Norma B; Anderson, Lynda A

2015-08-20

Addressing chronic disease burden requires the creation of collaborative networks to promote systemic changes and engage stakeholders. Although many such networks exist, they are rarely assessed with tools that account for their complexity. This study examined the structure of mentorship and collaboration relationships among members of the Healthy Aging Research Network (HAN) using social network analysis (SNA). We invited 97 HAN members and partners to complete an online social network survey that included closed-ended questions about HAN-specific mentorship and collaboration during the previous 12 months. Collaboration was measured by examining the activity of the network on 6 types of products: published articles, in-progress manuscripts, grant applications, tools, research projects, and presentations. We computed network-level measures such as density, number of components, and centralization to assess the cohesiveness of the network. Sixty-three respondents completed the survey (response rate, 65%). Responses, which included information about collaboration with nonrespondents, suggested that 74% of HAN members were connected through mentorship ties and that all 97 members were connected through at least one form of collaboration. Mentorship and collaboration ties were present both within and across boundaries of HAN member organizations. SNA of public health collaborative networks provides understanding about the structure of relationships that are formed as a result of participation in network activities. This approach may offer members and funders a way to assess the impact of such networks that goes beyond simply measuring products and participation at the individual level.

Network structure and influence of the climate change counter-movement

NASA Astrophysics Data System (ADS)

Farrell, Justin

2016-04-01

Anthropogenic climate change represents a global threat to human well-being and ecosystem functioning. Yet despite its importance for science and policy, our understanding of the causes of widespread uncertainty and doubt found among the general public remains limited. The political and social processes driving such doubt and uncertainty are difficult to rigorously analyse, and research has tended to focus on the individual-level, rather than the larger institutions and social networks that produce and disseminate contrarian information. This study presents a new approach by using network science to uncover the institutional and corporate structure of the climate change counter-movement, and machine-learning text analysis to show its influence in the news media and bureaucratic politics. The data include a new social network of all known organizations and individuals promoting contrarian viewpoints, as well as the entirety of all written and verbal texts about climate change from 1993-2013 from every organization, three major news outlets, all US presidents, and every occurrence on the floor of the US Congress. Using network and computational text analysis, I find that the organizational power within the contrarian network, and the magnitude of semantic similarity, are both predicted by ties to elite corporate benefactors.

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

PubMed Central

2014-01-01

Background 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. Methods 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. Results 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. Conclusions 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. PMID:24884864

Pragmatically Framed Cross-Situational Noun Learning Using Computational Reinforcement Models

PubMed Central

Najnin, Shamima; Banerjee, Bonny

2018-01-01

Cross-situational learning and social pragmatic theories are prominent mechanisms for learning word meanings (i.e., word-object pairs). In this paper, the role of reinforcement is investigated for early word-learning by an artificial agent. When exposed to a group of speakers, the agent comes to understand an initial set of vocabulary items belonging to the language used by the group. Both cross-situational learning and social pragmatic theory are taken into account. As social cues, joint attention and prosodic cues in caregiver's speech are considered. During agent-caregiver interaction, the agent selects a word from the caregiver's utterance and learns the relations between that word and the objects in its visual environment. The “novel words to novel objects” language-specific constraint is assumed for computing rewards. The models are learned by maximizing the expected reward using reinforcement learning algorithms [i.e., table-based algorithms: Q-learning, SARSA, SARSA-λ, and neural network-based algorithms: Q-learning for neural network (Q-NN), neural-fitted Q-network (NFQ), and deep Q-network (DQN)]. Neural network-based reinforcement learning models are chosen over table-based models for better generalization and quicker convergence. Simulations are carried out using mother-infant interaction CHILDES dataset for learning word-object pairings. Reinforcement is modeled in two cross-situational learning cases: (1) with joint attention (Attentional models), and (2) with joint attention and prosodic cues (Attentional-prosodic models). Attentional-prosodic models manifest superior performance to Attentional ones for the task of word-learning. The Attentional-prosodic DQN outperforms existing word-learning models for the same task. PMID:29441027

Coordinating complex problem-solving among distributed intelligent agents

NASA Technical Reports Server (NTRS)

Adler, Richard M.

1992-01-01

A process-oriented control model is described for distributed problem solving. The model coordinates the transfer and manipulation of information across independent networked applications, both intelligent and conventional. The model was implemented using SOCIAL, a set of object-oriented tools for distributing computing. Complex sequences of distributed tasks are specified in terms of high level scripts. Scripts are executed by SOCIAL objects called Manager Agents, which realize an intelligent coordination model that routes individual tasks to suitable server applications across the network. These tools are illustrated in a prototype distributed system for decision support of ground operations for NASA's Space Shuttle fleet.

Epidemic dynamics and endemic states in complex networks

NASA Astrophysics Data System (ADS)

Pastor-Satorras, Romualdo; Vespignani, Alessandro

2001-06-01

We study by analytical methods and large scale simulations a dynamical model for the spreading of epidemics in complex networks. In networks with exponentially bounded connectivity we recover the usual epidemic behavior with a threshold defining a critical point below that the infection prevalence is null. On the contrary, on a wide range of scale-free networks we observe the absence of an epidemic threshold and its associated critical behavior. This implies that scale-free networks are prone to the spreading and the persistence of infections whatever spreading rate the epidemic agents might possess. These results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks.

Local Area Network: Community Involvement, Social Capital, and Glocalization at NetU

ERIC Educational Resources Information Center

Trevett-Smith, Matthew D.

2010-01-01

Ethnographic and interview data from a long-term study of "NetU," a wired community and college, are used to investigate the effects of computer-mediated communication on social relationships. During the course of this research "LAN" residents of NetU are compared with a similar group of non-LAN residents who lived in the same neighborhood, but…

Teaching with technology: automatically receiving information from the internet and web.

PubMed

Wink, Diane M

2010-01-01

In this bimonthly series, the author examines how nurse educators can use the Internet and Web-based computer technologies such as search, communication, and collaborative writing tools, social networking and social bookmarking sites, virtual worlds, and Web-based teaching and learning programs. This article presents information and tools related to automatically receiving information from the Internet and Web.

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.

Measuring the value of accurate link prediction for network seeding.

PubMed

Wei, Yijin; Spencer, Gwen

2017-01-01

The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often assume perfect knowledge of the network topology. Unfortunately, social network links are rarely known in an exact way. When do seeding strategies based on less-than-accurate link prediction provide valuable insight? We introduce optimized-against-a-sample ([Formula: see text]) performance to measure the value of optimizing seeding based on a noisy observation of a network. Our computational study investigates [Formula: see text] under several threshold-spread models in synthetic and real-world networks. Our focus is on measuring the value of imprecise link information. The level of investment in link prediction that is strategic appears to depend closely on spread model: in some parameter ranges investments in improving link prediction can pay substantial premiums in cascade size. For other ranges, such investments would be wasted. Several trends were remarkably consistent across topologies.

Social Networks, Engagement and Resilience in University Students.

PubMed

Fernández-Martínez, Elena; Andina-Díaz, Elena; Fernández-Peña, Rosario; García-López, Rosa; Fulgueiras-Carril, Iván; Liébana-Presa, Cristina

2017-12-01

Analysis of social networks may be a useful tool for understanding the relationship between resilience and engagement, and this could be applied to educational methodologies, not only to improve academic performance, but also to create emotionally sustainable networks. This descriptive study was carried out on 134 university students. We collected the network structural variables, degree of resilience (CD-RISC 10), and engagement (UWES-S). The computer programs used were excel, UCINET for network analysis, and SPSS for statistical analysis. The analysis revealed results of means of 28.61 for resilience, 2.98 for absorption, 4.82 for dedication, and 3.13 for vigour. The students had two preferred places for sharing information: the classroom and WhatsApp. The greater the value for engagement, the greater the degree of centrality in the friendship network among students who are beginning their university studies. This relationship becomes reversed as the students move to later academic years. In terms of resilience, the highest values correspond to greater centrality in the friendship networks. The variables of engagement and resilience influenced the university students' support networks.

Social Networks, Engagement and Resilience in University Students

PubMed Central

García-López, Rosa; Fulgueiras-Carril, Iván

2017-01-01

Analysis of social networks may be a useful tool for understanding the relationship between resilience and engagement, and this could be applied to educational methodologies, not only to improve academic performance, but also to create emotionally sustainable networks. This descriptive study was carried out on 134 university students. We collected the network structural variables, degree of resilience (CD-RISC 10), and engagement (UWES-S). The computer programs used were excel, UCINET for network analysis, and SPSS for statistical analysis. The analysis revealed results of means of 28.61 for resilience, 2.98 for absorption, 4.82 for dedication, and 3.13 for vigour. The students had two preferred places for sharing information: the classroom and WhatsApp. The greater the value for engagement, the greater the degree of centrality in the friendship network among students who are beginning their university studies. This relationship becomes reversed as the students move to later academic years. In terms of resilience, the highest values correspond to greater centrality in the friendship networks. The variables of engagement and resilience influenced the university students’ support networks. PMID:29194361

"I like talking to people on the computer": Outcomes of a home-based intervention to develop social media skills in youth with disabilities living in rural communities.

PubMed

Raghavendra, Parimala; Hutchinson, Claire; Grace, Emma; Wood, Denise; Newman, Lareen

2018-05-01

To investigate the effectiveness of a home-based social media use intervention to enhance the social networks of rural youth with disabilities. Participants were nine youth (mean age = 17.0 years) with disabilities from two rural Australian communities. The intervention consisted of providing appropriate assistive technology and social media training on individualised goals. Using mixed methods, quantitative (a single group pre-post) and qualitative (interviews with participants and their carers) measures were used to examine outcomes of training, individual experiences of the intervention, and changes to online social networks. Participants increased their performance and satisfaction with performance on social media problem areas post-intervention; paired t-tests showed statistical significance at p < .001. There was also a significant increase in the number of online communication partners; Wilcoxon Signed Ranks showed statistical significance at p < .05. The interviews highlighted increased social participation, independence and improvements to literacy. Ongoing parental concerns regarding cyber safety and inappropriate online content were noted. The findings suggest that social media training is a feasible method for increasing social networks among rural-based youth with disabilities. To sustain ongoing benefits, parents need knowledge and training in integrating assistive technology and social media. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Handicap Principle for Trust in Computer Security, the Semantic Web and Social Networking

NASA Astrophysics Data System (ADS)

Ma, Zhanshan (Sam); Krings, Axel W.; Hung, Chih-Cheng

Communication is a fundamental function of life, and it exists in almost all living things: from single-cell bacteria to human beings. Communication, together with competition and cooperation,arethree fundamental processes in nature. Computer scientists are familiar with the study of competition or 'struggle for life' through Darwin's evolutionary theory, or even evolutionary computing. They may be equally familiar with the study of cooperation or altruism through the Prisoner's Dilemma (PD) game. However, they are likely to be less familiar with the theory of animal communication. The objective of this article is three-fold: (i) To suggest that the study of animal communication, especially the honesty (reliability) of animal communication, in which some significant advances in behavioral biology have been achieved in the last three decades, should be on the verge to spawn important cross-disciplinary research similar to that generated by the study of cooperation with the PD game. One of the far-reaching advances in the field is marked by the publication of "The Handicap Principle: a Missing Piece of Darwin's Puzzle" by Zahavi (1997). The 'Handicap' principle [34][35], which states that communication signals must be costly in some proper way to be reliable (honest), is best elucidated with evolutionary games, e.g., Sir Philip Sidney (SPS) game [23]. Accordingly, we suggest that the Handicap principle may serve as a fundamental paradigm for trust research in computer science. (ii) To suggest to computer scientists that their expertise in modeling computer networks may help behavioral biologists in their study of the reliability of animal communication networks. This is largely due to the historical reason that, until the last decade, animal communication was studied with the dyadic paradigm (sender-receiver) rather than with the network paradigm. (iii) To pose several open questions, the answers to which may bear some refreshing insights to trust research in computer science, especially secure and resilient computing, the semantic web, and social networking. One important thread unifying the three aspects is the evolutionary game theory modeling or its extensions with survival analysis and agreement algorithms [19][20], which offer powerful game models for describing time-, space-, and covariate-dependent frailty (uncertainty and vulnerability) and deception (honesty).

Bose-Einstein condensates form in heuristics learned by ciliates deciding to signal 'social' commitments.

PubMed

Clark, Kevin B

2010-03-01

Fringe quantum biology theories often adopt the concept of Bose-Einstein condensation when explaining how consciousness, emotion, perception, learning, and reasoning emerge from operations of intact animal nervous systems and other computational media. However, controversial empirical evidence and mathematical formalism concerning decoherence rates of bioprocesses keep these frameworks from satisfactorily accounting for the physical nature of cognitive-like events. This study, inspired by the discovery that preferential attachment rules computed by complex technological networks obey Bose-Einstein statistics, is the first rigorous attempt to examine whether analogues of Bose-Einstein condensation precipitate learned decision making in live biological systems as bioenergetics optimization predicts. By exploiting the ciliate Spirostomum ambiguum's capacity to learn and store behavioral strategies advertising mating availability into heuristics of topologically invariant computational networks, three distinct phases of strategy use were found to map onto statistical distributions described by Bose-Einstein, Fermi-Dirac, and classical Maxwell-Boltzmann behavior. Ciliates that sensitized or habituated signaling patterns to emit brief periods of either deceptive 'harder-to-get' or altruistic 'easier-to-get' serial escape reactions began testing condensed on initially perceived fittest 'courting' solutions. When these ciliates switched from their first strategy choices, Bose-Einstein condensation of strategy use abruptly dissipated into a Maxwell-Boltzmann computational phase no longer dominated by a single fittest strategy. Recursive trial-and-error strategy searches annealed strategy use back into a condensed phase consistent with performance optimization. 'Social' decisions performed by ciliates showing no nonassociative learning were largely governed by Fermi-Dirac statistics, resulting in degenerate distributions of strategy choices. These findings corroborate previous work demonstrating ciliates with improving expertise search grouped 'courting' assurances at quantum efficiencies and verify efficient processing by primitive 'social' intelligences involves network forms of Bose-Einstein condensation coupled to preceding thermodynamic-sensitive computational phases. 2009 Elsevier Ireland Ltd. All rights reserved.

Research for the design of visual fatigue based on the computer visual communication

NASA Astrophysics Data System (ADS)

Deng, Hu-Bin; Ding, Bao-min

2013-03-01

With the era of rapid development of computer networks. The role of network communication in the social, economic, political, become more and more important and suggested their special role. The computer network communicat ion through the modern media and byway of the visual communication effect the public of the emotional, spiritual, career and other aspects of the life. While its rapid growth also brought some problems, It is that their message across to the public, its design did not pass a relat ively perfect manifestation to express the informat ion. So this not only leads to convey the error message, but also to cause the physical and psychological fatigue for the audiences. It is said that the visual fatigue. In order to reduce the fatigue when people obtain the useful information in using computer. Let the audience in a short time to obtain the most useful informat ion, this article gave a detailed account of its causes, and propose effective solutions and, through the specific examples to explain it, also in the future computer design visual communicat ion applications development prospect.

78 FR 40476 - Information Collection Being Reviewed by the Federal Communications Commission

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-05

... social networking Web sites and in chat rooms and cyber bullying awareness and response (as required by... schools and libraries that have computers with Internet access must certify that they have in place...

The Effect of Social Grounding on Collaboration in a Computer-Mediated Small Group Discussion

ERIC Educational Resources Information Center

Ahern, Terence C.; Thomas, Julie A.; Tallent-Runnels, Mary K.; Lan, William Y.; Cooper, Sandra; Lu, Xiaoming; Cyrus, Jacqui

2006-01-01

There is a tremendous amount of pressure on educators to incorporate highly advanced computer-mediated communication (CMC) into the classroom, but the research shows that this is not an easy task. Part of the difficulty learners experience within current network applications is a lack of support from the design of the software for the development…

Human Computer Interaction (HCI) and Internet Residency: Implications for Both Personal Life and Teaching/Learning

ERIC Educational Resources Information Center

Crearie, Linda

2016-01-01

Technological advances over the last decade have had a significant impact on the teaching and learning experiences students encounter today. We now take technologies such as Web 2.0, mobile devices, cloud computing, podcasts, social networking, super-fast broadband, and connectedness for granted. So what about the student use of these types of…

Designing Smart Artifacts for Adaptive Mediation of Social Viscosity: Triadic Actor-Network Enactments as a Basis for Interaction Design

ERIC Educational Resources Information Center

Salamanca, Juan

2012-01-01

With the advent of ubiquitous computing, interaction design has broadened its object of inquiry into how smart computational artifacts inconspicuously act in people's everyday lives. Although user-centered design approaches remains useful for exploring how people cope with interactive systems, they cannot explain how this new breed of…

On Trust Evaluation in Mobile Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Nguyen, Dang Quan; Lamont, Louise; Mason, Peter C.

Trust has been considered as a social relationship between two individuals in human society. But, as computer science and networking have succeeded in using computers to automate many tasks, the concept of trust can be generalized to cover the reliability and relationships of non-human interaction, such as, for example, information gathering and data routing. This paper investigates the evaluation of trust in the context of ad hoc networks. Nodes evaluate each other’s behaviour based on observables. A node then decides whether to trust another node to have certain innate abilities. We show how accurate such an evaluation could be. We also provide the minimum number of observations required to obtain an accurate evaluation, a result that indicates that observation-based trust in ad hoc networks will remain a challenging problem. The impact of making networking decisions using trust evaluation on the network connectivity is also examined. In this manner, quantitative decisions can be made concerning trust-based routing with the knowledge of the potential impact on connectivity.

Network biology: Describing biological systems by complex networks. Comment on "Network science of biological systems at different scales: A review" by M. Gosak et al.

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2018-03-01

I enjoyed reading Gosak et al. review on analysing biological systems from network science perspective [1]. Network science, first started within Physics community, is now a mature multidisciplinary field of science with many applications ranging from Ecology to biology, medicine, social sciences, engineering and computer science. Gosak et al. discussed how biological systems can be modelled and described by complex network theory which is an important application of network science. Although there has been considerable progress in network biology over the past two decades, this is just the beginning and network science has a great deal to offer to biology and medical sciences.

Cost effective campaigning in social networks

NASA Astrophysics Data System (ADS)

Kotnis, Bhushan; Kuri, Joy

2016-05-01

Campaigners are increasingly using online social networking platforms for promoting products, ideas and information. A popular method of promoting a product or even an idea is incentivizing individuals to evangelize the idea vigorously by providing them with referral rewards in the form of discounts, cash backs, or social recognition. Due to budget constraints on scarce resources such as money and manpower, it may not be possible to provide incentives for the entire population, and hence incentives need to be allocated judiciously to appropriate individuals for ensuring the highest possible outreach size. We aim to do the same by formulating and solving an optimization problem using percolation theory. In particular, we compute the set of individuals that are provided incentives for minimizing the expected cost while ensuring a given outreach size. We also solve the problem of computing the set of individuals to be incentivized for maximizing the outreach size for given cost budget. The optimization problem turns out to be non trivial; it involves quantities that need to be computed by numerically solving a fixed point equation. Our primary contribution is, that for a fairly general cost structure, we show that the optimization problems can be solved by solving a simple linear program. We believe that our approach of using percolation theory to formulate an optimization problem is the first of its kind.

A synthetic computational environment: To control the spread of respiratory infections in a virtual university

NASA Astrophysics Data System (ADS)

Ge, Yuanzheng; Chen, Bin; liu, Liang; Qiu, Xiaogang; Song, Hongbin; Wang, Yong

2018-02-01

Individual-based computational environment provides an effective solution to study complex social events by reconstructing scenarios. Challenges remain in reconstructing the virtual scenarios and reproducing the complex evolution. In this paper, we propose a framework to reconstruct a synthetic computational environment, reproduce the epidemic outbreak, and evaluate management interventions in a virtual university. The reconstructed computational environment includes 4 fundamental components: the synthetic population, behavior algorithms, multiple social networks, and geographic campus environment. In the virtual university, influenza H1N1 transmission experiments are conducted, and gradually enhanced interventions are evaluated and compared quantitatively. The experiment results indicate that the reconstructed virtual environment provides a solution to reproduce complex emergencies and evaluate policies to be executed in the real world.

Computing the Social Brain Connectome Across Systems and States.

PubMed

Alcalá-López, Daniel; Smallwood, Jonathan; Jefferies, Elizabeth; Van Overwalle, Frank; Vogeley, Kai; Mars, Rogier B; Turetsky, Bruce I; Laird, Angela R; Fox, Peter T; Eickhoff, Simon B; Bzdok, Danilo

2017-05-18

Social skills probably emerge from the interaction between different neural processing levels. However, social neuroscience is fragmented into highly specialized, rarely cross-referenced topics. The present study attempts a systematic reconciliation by deriving a social brain definition from neural activity meta-analyses on social-cognitive capacities. The social brain was characterized by meta-analytic connectivity modeling evaluating coactivation in task-focused brain states and physiological fluctuations evaluating correlations in task-free brain states. Network clustering proposed a functional segregation into (1) lower sensory, (2) limbic, (3) intermediate, and (4) high associative neural circuits that together mediate various social phenomena. Functional profiling suggested that no brain region or network is exclusively devoted to social processes. Finally, nodes of the putative mirror-neuron system were coherently cross-connected during tasks and more tightly coupled to embodied simulation systems rather than abstract emulation systems. These first steps may help reintegrate the specialized research agendas in the social and affective sciences. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information

PubMed Central

Jang, Haeran

2014-01-01

Objectives Utilizing social network analysis, this study aimed to analyze the main keywords in the literature regarding the health literacy of and the use of online health information by aged persons over 65. Methods Medical Subject Heading keywords were extracted from articles on the PubMed database of the National Library of Medicine. For health literacy, 110 articles out of 361 were initially extracted. Seventy-one keywords out of 1,021 were finally selected after removing repeated keywords and applying pruning. Regarding the use of online health information, 19 articles out of 26 were selected. One hundred forty-four keywords were initially extracted. After removing the repeated keywords, 74 keywords were finally selected. Results Health literacy was found to be strongly connected with 'Health knowledge, attitudes, practices' and 'Patient education as topic.' 'Computer literacy' had strong connections with 'Internet' and 'Attitude towards computers.' 'Computer literacy' was connected to 'Health literacy,' and was studied according to the parameters 'Attitude towards health' and 'Patient education as topic.' The use of online health information was strongly connected with 'Health knowledge, attitudes, practices,' 'Consumer health information,' 'Patient education as topic,' etc. In the network, 'Computer literacy' was connected with 'Health education,' 'Patient satisfaction,' 'Self-efficacy,' 'Attitude to computer,' etc. Conclusions Research on older citizens' health literacy and their use of online health information was conducted together with study of computer literacy, patient education, attitude towards health, health education, patient satisfaction, etc. In particular, self-efficacy was noted as an important keyword. Further research should be conducted to identify the effective outcomes of self-efficacy in the area of interest. PMID:25152835

Can social networking be used to promote engagement in child maltreatment prevention programs? Two pilot studies.

PubMed

Edwards-Gaura, Anna; Whitaker, Daniel; Self-Brown, Shannon

2014-08-01

Child maltreatment is one of the United States' most significant public health problems. In efforts to prevent maltreatment experts recommend use of Behavioral Parent Training Programs (BPTs), which focus on teaching skills that will replace and prevent maltreating behavior. While there is research to support the effectiveness of BPTs in maltreatment prevention, the reach of such programs is still limited by several barriers, including poor retention of families in services. Recently, new technologies have emerged that offer innovative opportunities to improve family engagement. These technologies include smartphones and social networking; however, very little is known about the potential of these to aid in maltreatment prevention. The primary goal of this study was to conduct 2 pilot exploratory projects. The first project administered a survey to parents and providers to gather data about at-risk parents' use of smartphones and online social networking technologies. The second project tested a social networking-enhanced brief parenting program with 3 intervention participants and evaluated parental responses. Seventy-five percent of parents surveyed reported owning a computer that worked. Eighty-nine percent of parents reported that they had reliable Internet access at home, and 67% said they used the Internet daily. Three parents participated in the intervention with all reporting improvement in parent-child interaction skills and a positive experience participating in the social networking-enhanced SafeCare components. In general, findings suggest that smartphones, social networking, and Facebook, in particular, are now being used by individuals who show risk factors for maltreatment. Further, the majority of parents surveyed in this study said that they like Facebook, and all parents surveyed said that they use Facebook and have a Facebook account. As well, all saw it as a potentially beneficial supplement for future parents enrolling in parenting programs.

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.

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.

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.

Teaching with technology: free Web resources for teaching and learning.

PubMed

Wink, Diane M; Smith-Stoner, Marilyn

2011-01-01

In this bimonthly series, the department editor examines how nurse educators can use Internet and Web-based computer technologies such as search, communication, collaborative writing tools; social networking, and social bookmarking sites; virtual worlds; and Web-based teaching and learning programs. In this article, the department editor and her coauthor describe free Web-based resources that can be used to support teaching and learning.

Analysis of context dependence in social interaction networks of a massively multiplayer online role-playing game.

PubMed

Son, Seokshin; Kang, Ah Reum; Kim, Hyun-chul; Kwon, Taekyoung; Park, Juyong; Kim, Huy Kang

2012-01-01

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior.

Introduction

NASA Astrophysics Data System (ADS)

Dum, Ralph

Various types of diverse networks — communication networks, transport networks, global business networks, networks of friends, or the Internet — shape our daily life and the way we think and act. We depend on various social, economic, and technological networks that weave a tissue of businesses, governments, technologies and that contain us as citizens, users, or customers. We only become aware of our dependence if failures occur in these networks: when cities are plunged into darkness because of a breakdown of the power grid like happened recently in New York, when national economies collapse because of a failure of global financial systems like happened in the South-Asian banking crisis, or when computer viruses spread with mind-boggling speed over information networks destroying or, even worse, exposing sensitive data.

Real-Time Joint Streaming Data Processing from Social and Physical Sensors

NASA Astrophysics Data System (ADS)

Kropivnitskaya, Y. Y.; Qin, J.; Tiampo, K. F.; Bauer, M.

2014-12-01

The results of the technological breakthroughs in computing that have taken place over the last few decades makes it possible to achieve emergency management objectives that focus on saving human lives and decreasing economic effects. In particular, the integration of a wide variety of information sources, including observations from spatially-referenced physical sensors and new social media sources, enables better real-time seismic hazard analysis through distributed computing networks. The main goal of this work is to utilize innovative computational algorithms for better real-time seismic risk analysis by integrating different data sources and processing tools into streaming and cloud computing applications. The Geological Survey of Canada operates the Canadian National Seismograph Network (CNSN) with over 100 high-gain instruments and 60 low-gain or strong motion seismographs. The processing of the continuous data streams from each station of the CNSN provides the opportunity to detect possible earthquakes in near real-time. The information from physical sources is combined to calculate a location and magnitude for an earthquake. The automatically calculated results are not always sufficiently precise and prompt that can significantly reduce the response time to a felt or damaging earthquake. Social sensors, here represented as Twitter users, can provide information earlier to the general public and more rapidly to the emergency planning and disaster relief agencies. We introduce joint streaming data processing from social and physical sensors in real-time based on the idea that social media observations serve as proxies for physical sensors. By using the streams of data in the form of Twitter messages, each of which has an associated time and location, we can extract information related to a target event and perform enhanced analysis by combining it with physical sensor data. Results of this work suggest that the use of data from social media, in conjunction with the development of innovative computing algorithms, when combined with sensor data can provide a new paradigm for real-time earthquake detection in order to facilitate rapid and inexpensive natural risk reduction.

A spectral method to detect community structure based on distance modularity matrix

NASA Astrophysics Data System (ADS)

Yang, Jin-Xuan; Zhang, Xiao-Dong

2017-08-01

There are many community organizations in social and biological networks. How to identify these community structure in complex networks has become a hot issue. In this paper, an algorithm to detect community structure of networks is proposed by using spectra of distance modularity matrix. The proposed algorithm focuses on the distance of vertices within communities, rather than the most weakly connected vertex pairs or number of edges between communities. The experimental results show that our method achieves better effectiveness to identify community structure for a variety of real-world networks and computer generated networks with a little more time-consumption.

Assortativity Patterns in Multi-dimensional Inter-organizational Networks: A Case Study of the Humanitarian Relief Sector

NASA Astrophysics Data System (ADS)

Zhao, Kang; Ngamassi, Louis-Marie; Yen, John; Maitland, Carleen; Tapia, Andrea

We use computational tools to study assortativity patterns in multi-dimensional inter-organizational networks on the basis of different node attributes. In the case study of an inter-organizational network in the humanitarian relief sector, we consider not only macro-level topological patterns, but also assortativity on the basis of micro-level organizational attributes. Unlike assortative social networks, this inter-organizational network exhibits disassortative or random patterns on three node attributes. We believe organizations' seek of complementarity is one of the main reasons for the special patterns. Our analysis also provides insights on how to promote collaborations among the humanitarian relief organizations.

Fitting ERGMs on big networks.

PubMed

An, Weihua

2016-09-01

The exponential random graph model (ERGM) has become a valuable tool for modeling social networks. In particular, ERGM provides great flexibility to account for both covariates effects on tie formations and endogenous network formation processes. However, there are both conceptual and computational issues for fitting ERGMs on big networks. This paper describes a framework and a series of methods (based on existent algorithms) to address these issues. It also outlines the advantages and disadvantages of the methods and the conditions to which they are most applicable. Selected methods are illustrated through examples. Copyright © 2016 Elsevier Inc. All rights reserved.

Conceptualizing and Advancing Research Networking Systems

PubMed Central

SCHLEYER, TITUS; BUTLER, BRIAN S.; SONG, MEI; SPALLEK, HEIKO

2013-01-01

Science in general, and biomedical research in particular, is becoming more collaborative. As a result, collaboration with the right individuals, teams, and institutions is increasingly crucial for scientific progress. We propose Research Networking Systems (RNS) as a new type of system designed to help scientists identify and choose collaborators, and suggest a corresponding research agenda. The research agenda covers four areas: foundations, presentation, architecture, and evaluation. Foundations includes project-, institution- and discipline-specific motivational factors; the role of social networks; and impression formation based on information beyond expertise and interests. Presentation addresses representing expertise in a comprehensive and up-to-date manner; the role of controlled vocabularies and folksonomies; the tension between seekers’ need for comprehensive information and potential collaborators’ desire to control how they are seen by others; and the need to support serendipitous discovery of collaborative opportunities. Architecture considers aggregation and synthesis of information from multiple sources, social system interoperability, and integration with the user’s primary work context. Lastly, evaluation focuses on assessment of collaboration decisions, measurement of user-specific costs and benefits, and how the large-scale impact of RNS could be evaluated with longitudinal and naturalistic methods. We hope that this article stimulates the human-computer interaction, computer-supported cooperative work, and related communities to pursue a broad and comprehensive agenda for developing research networking systems. PMID:24376309

Conceptualizing and Advancing Research Networking Systems.

PubMed

Schleyer, Titus; Butler, Brian S; Song, Mei; Spallek, Heiko

2012-03-01

Science in general, and biomedical research in particular, is becoming more collaborative. As a result, collaboration with the right individuals, teams, and institutions is increasingly crucial for scientific progress. We propose Research Networking Systems (RNS) as a new type of system designed to help scientists identify and choose collaborators, and suggest a corresponding research agenda. The research agenda covers four areas: foundations, presentation, architecture , and evaluation . Foundations includes project-, institution- and discipline-specific motivational factors; the role of social networks; and impression formation based on information beyond expertise and interests. Presentation addresses representing expertise in a comprehensive and up-to-date manner; the role of controlled vocabularies and folksonomies; the tension between seekers' need for comprehensive information and potential collaborators' desire to control how they are seen by others; and the need to support serendipitous discovery of collaborative opportunities. Architecture considers aggregation and synthesis of information from multiple sources, social system interoperability, and integration with the user's primary work context. Lastly, evaluation focuses on assessment of collaboration decisions, measurement of user-specific costs and benefits, and how the large-scale impact of RNS could be evaluated with longitudinal and naturalistic methods. We hope that this article stimulates the human-computer interaction, computer-supported cooperative work, and related communities to pursue a broad and comprehensive agenda for developing research networking systems.

"Us and them": a social network analysis of physicians' professional networks and their attitudes towards EBM.

PubMed

Mascia, Daniele; Cicchetti, Americo; Damiani, Gianfranco

2013-10-22

Extant research suggests that there is a strong social component to Evidence-Based Medicine (EBM) adoption since professional networks amongst physicians are strongly associated with their attitudes towards EBM. Despite this evidence, it is still unknown whether individual attitudes to use scientific evidence in clinical decision-making influence the position that physicians hold in their professional network. This paper explores how physicians' attitudes towards EBM is related to the network position they occupy within healthcare organizations. Data pertain to a sample of Italian physicians, whose professional network relationships, demographics and work-profile characteristics were collected. A social network analysis was performed to capture the structural importance of physicians in the collaboration network by the means of a core-periphery analysis and the computation of network centrality indicators. Then, regression analysis was used to test the association between the network position of individual clinicians and their attitudes towards EBM. Findings documented that the overall network structure is made up of a dense cohesive core of physicians and of less connected clinicians who occupy the periphery. A negative association between the physicians' attitudes towards EBM and the coreness they exhibited in the professional network was also found. Network centrality indicators confirmed these results documenting a negative association between physicians' propensity to use EBM and their structural importance in the professional network. Attitudes that physicians show towards EBM are related to the part (core or periphery) of the professional networks to which they belong as well as to their structural importance. By identifying virtuous attitudes and behaviors of professionals within their organizations, policymakers and executives may avoid marginalization and stimulate integration and continuity of care, both within and across the boundaries of healthcare providers.

Social Computing and the Attention Economy

NASA Astrophysics Data System (ADS)

Huberman, Bernardo A.

2013-04-01

Social computing focuses on the interaction between social behavior and information, especially on how the latter propagates across social networks and is consumed and transformed in the process. At the same time the ubiquity of information has left it devoid of much monetary value. The scarce, and therefore valuable, resource is now attention, and its allocation gives rise to an attention economy that determines how content is consumed and propagated. Since two major factors involved in getting attention are novelty and popularity, we analyze the role that both play in attracting attention to web content and how to prioritize them in order to maximize it. We also demonstrate that the relative performance of strategies based on prioritizing either popularity or novelty exhibit an abrupt change around a critical value of the novelty decay time, resembling a phase transition.

Privacy and the National Information Infrastructure.

ERIC Educational Resources Information Center

Rotenberg, Marc

1994-01-01

Explains the work of Computer Professionals for Social Responsibility regarding privacy issues in the use of electronic networks; recommends principles that should be adopted for a National Information Infrastructure privacy code; discusses the need for public education; and suggests pertinent legislative proposals. (LRW)

Virality Prediction and Community Structure in Social Networks

NASA Astrophysics Data System (ADS)

Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

2013-08-01

How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

Virality Prediction and Community Structure in Social Networks

PubMed Central

Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

2013-01-01

How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications. PMID:23982106

Virality prediction and community structure in social networks.

PubMed

Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

2013-01-01

How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

Network dynamics of social influence in the wisdom of crowds

PubMed Central

Brackbill, Devon; Centola, Damon

2017-01-01

A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton’s discovery of the “wisdom of crowds” [Galton F (1907) Nature 75:450–451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals’ estimates became more similar when subjects observed each other’s beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020–9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error. PMID:28607070

Network dynamics of social influence in the wisdom of crowds.

PubMed

Becker, Joshua; Brackbill, Devon; Centola, Damon

2017-06-27

A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton's discovery of the "wisdom of crowds" [Galton F (1907) Nature 75:450-451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies ]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals' estimates became more similar when subjects observed each other's beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020-9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error.

Correlations between Community Structure and Link Formation in Complex Networks

PubMed Central

Liu, Zhen; He, Jia-Lin; Kapoor, Komal; Srivastava, Jaideep

2013-01-01

Background Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. Methodology/Principal Findings Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. Conclusions/Significance Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction. PMID:24039818

CP-ABE Based Privacy-Preserving User Profile Matching in Mobile Social Networks

PubMed Central

Cui, Weirong; Du, Chenglie; Chen, Jinchao

2016-01-01

Privacy-preserving profile matching, a challenging task in mobile social networks, is getting more attention in recent years. In this paper, we propose a novel scheme that is based on ciphertext-policy attribute-based encryption to tackle this problem. In our scheme, a user can submit a preference-profile and search for users with matching-profile in decentralized mobile social networks. In this process, no participant’s profile and the submitted preference-profile is exposed. Meanwhile, a secure communication channel can be established between the pair of successfully matched users. In contrast to existing related schemes which are mainly based on the secure multi-party computation, our scheme can provide verifiability (both the initiator and any unmatched user cannot cheat each other to pretend to be matched), and requires few interactions among users. We provide thorough security analysis and performance evaluation on our scheme, and show its advantages in terms of security, efficiency and usability over state-of-the-art schemes. PMID:27337001

CP-ABE Based Privacy-Preserving User Profile Matching in Mobile Social Networks.

PubMed

Cui, Weirong; Du, Chenglie; Chen, Jinchao

2016-01-01

Privacy-preserving profile matching, a challenging task in mobile social networks, is getting more attention in recent years. In this paper, we propose a novel scheme that is based on ciphertext-policy attribute-based encryption to tackle this problem. In our scheme, a user can submit a preference-profile and search for users with matching-profile in decentralized mobile social networks. In this process, no participant's profile and the submitted preference-profile is exposed. Meanwhile, a secure communication channel can be established between the pair of successfully matched users. In contrast to existing related schemes which are mainly based on the secure multi-party computation, our scheme can provide verifiability (both the initiator and any unmatched user cannot cheat each other to pretend to be matched), and requires few interactions among users. We provide thorough security analysis and performance evaluation on our scheme, and show its advantages in terms of security, efficiency and usability over state-of-the-art schemes.

Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media.

PubMed

Bail, Christopher Andrew

2016-10-18

Social media sites are rapidly becoming one of the most important forums for public deliberation about advocacy issues. However, social scientists have not explained why some advocacy organizations produce social media messages that inspire far-ranging conversation among social media users, whereas the vast majority of them receive little or no attention. I argue that advocacy organizations are more likely to inspire comments from new social media audiences if they create "cultural bridges," or produce messages that combine conversational themes within an advocacy field that are seldom discussed together. I use natural language processing, network analysis, and a social media application to analyze how cultural bridges shaped public discourse about autism spectrum disorders on Facebook over the course of 1.5 years, controlling for various characteristics of advocacy organizations, their social media audiences, and the broader social context in which they interact. I show that organizations that create substantial cultural bridges provoke 2.52 times more comments about their messages from new social media users than those that do not, controlling for these factors. This study thus offers a theory of cultural messaging and public deliberation and computational techniques for text analysis and application-based survey research.

Individual and Network Interventions With Injection Drug Users in 5 Ukraine Cities

PubMed Central

Lehman, Wayne E. K.; Latkin, Carl A.; Dvoryak, Sergey; Brewster, John T.; Royer, Mark S.; Sinitsyna, Larisa

2011-01-01

Objectives. We evaluated the effects of an individual intervention versus a network intervention on HIV-related injection and sexual risk behaviors among street-recruited opiate injection drug users in 5 Ukraine cities. Methods. Between 2004 and 2006, 722 opiate injection drug users were recruited to participate in interventions that were either individually based or based on a social network model in which peer educators intervened with their network members. Audio computer-assisted self-interview techniques were used to interview participants at baseline and follow-up. Results. Multiple logistic analyses controlling for baseline injection and sexual risks revealed that both peer educators and network members in the network intervention reduced injection-related risk behaviors significantly more than did those in the individually based intervention and that peer educators increased condom use significantly more than did those in the individual intervention. Individual intervention participants, however, showed significantly greater improvements than did network members with respect to reductions in sexual risk behaviors. Conclusions. Social network interventions may be more effective than individually based interventions in changing injection risk behaviors among both peer educators and network members. The effectiveness of network interventions in changing sexual risk behaviors is less clear, probably owing to network composition and inhibitions regarding discussing sexual risk behaviors. PMID:20395584

The social brain: allowing humans to boldly go where no other species has been

PubMed Central

Frith, Uta; Frith, Chris

2010-01-01

The biological basis of complex human social interaction and communication has been illuminated through a coming together of various methods and disciplines. Among these are comparative studies of other species, studies of disorders of social cognition and developmental psychology. The use of neuroimaging and computational models has given weight to speculations about the evolution of social behaviour and culture in human societies. We highlight some networks of the social brain relevant to two-person interactions and consider the social signals between interacting partners that activate these networks. We make a case for distinguishing between signals that automatically trigger interaction and cooperation and ostensive signals that are used deliberately. We suggest that this ostensive signalling is needed for ‘closing the loop’ in two-person interactions, where the partners each know that they have the intention to communicate. The use of deliberate social signals can serve to increase reputation and trust and facilitates teaching. This is likely to be a critical factor in the steep cultural ascent of mankind. PMID:20008394

Modeling the Emergence of Lexicons in Homesign Systems

PubMed Central

Richie, Russell; Yang, Charles; Coppola, Marie

2014-01-01

It is largely acknowledged that natural languages emerge from not just human brains, but also from rich communities of interacting human brains (Senghas, 2005). Yet the precise role of such communities and such interaction in the emergence of core properties of language has largely gone uninvestigated in naturally emerging systems, leaving the few existing computational investigations of this issue at an artificial setting. Here we take a step towards investigating the precise role of community structure in the emergence of linguistic conventions with both naturalistic empirical data and computational modeling. We first show conventionalization of lexicons in two different classes of naturally emerging signed systems: (1) protolinguistic “homesigns” invented by linguistically isolated Deaf individuals, and (2) a natural sign language emerging in a recently formed rich Deaf community. We find that the latter conventionalized faster than the former. Second, we model conventionalization as a population of interacting individuals who adjust their probability of sign use in response to other individuals' actual sign use, following an independently motivated model of language learning (Yang 2002, 2004). Simulations suggest that a richer social network, like that of natural (signed) languages, conventionalizes faster than a sparser social network, like that of homesign systems. We discuss our behavioral and computational results in light of other work on language emergence, and other work of behavior on complex networks. PMID:24482343

Experiences of women with breast cancer: exchanging social support over the CHESS computer network.

PubMed

Shaw, B R; McTavish, F; Hawkins, R; Gustafson, D H; Pingree, S

2000-01-01

Using an existential-phenomenological approach, this paper describes how women with breast cancer experience the giving and receiving of social support in a computer-mediated context. Women viewed their experiences with the computer-mediated support group as an additional and unique source of support in facing their illness. Anonymity within the support group fostered equalized participation and allowed women to communicate in ways that would have been more difficult in a face-to-face context. The asynchronous communication was a frustration to some participants, but some indicated that the format allowed for more thoughtful interaction. Motivations for seeking social support appeared to be a dynamic process, with a consistent progression from a position of receiving support to that of giving support. The primary benefits women received from participation in the group were communicating with other people who shared similar problems and helping others, which allowed them to change their focus from a preoccupation with their own sickness to thinking of others. Consistent with past research is the finding that women in this study expressed that social support is a multidimensional phenomenon and that their computer-mediated support group provided abundant emotional support, encouragement, and informational support. Excerpts from the phenomenological interviews are used to review and highlight key theoretical concepts from the research literatures on computer-mediated communication, social support, and the psychosocial needs of women with breast cancer.

Epidemic dynamics on a risk-based evolving social network

NASA Astrophysics Data System (ADS)

Antwi, Shadrack; Shaw, Leah

2013-03-01

Social network models have been used to study how behavior affects the dynamics of an infection in a population. Motivated by HIV, we consider how a trade-off between benefits and risks of sexual connections determine network structure and disease prevalence. We define a stochastic network model with formation and breaking of links as changes in sexual contacts. Each node has an intrinsic benefit its neighbors derive from connecting to it. Nodes' infection status is not apparent to others, but nodes with more connections (higher degree) are assumed more likely to be infected. The probability to form and break links is determined by a payoff computed from the benefit and degree-dependent risk. The disease is represented by a SI (susceptible-infected) model. We study network and epidemic evolution via Monte Carlo simulation and analytically predict the behavior with a heterogeneous mean field approach. The dependence of network connectivity and infection threshold on parameters is determined, and steady state degree distribution and epidemic levels are obtained. We also study a situation where system-wide infection levels alter perception of risk and cause nodes to adjust their behavior. This is a case of an adaptive network, where node status feeds back to change network geometry.

Reading wild minds: A computational assay of Theory of Mind sophistication across seven primate species

PubMed Central

Devaine, Marie; San-Galli, Aurore; Trapanese, Cinzia; Bardino, Giulia; Hano, Christelle; Saint Jalme, Michel; Bouret, Sebastien

2017-01-01

Theory of Mind (ToM), i.e. the ability to understand others' mental states, endows humans with highly adaptive social skills such as teaching or deceiving. Candidate evolutionary explanations have been proposed for the unique sophistication of human ToM among primates. For example, the Machiavellian intelligence hypothesis states that the increasing complexity of social networks may have induced a demand for sophisticated ToM. This type of scenario ignores neurocognitive constraints that may eventually be crucial limiting factors for ToM evolution. In contradistinction, the cognitive scaffolding hypothesis asserts that a species' opportunity to develop sophisticated ToM is mostly determined by its general cognitive capacity (on which ToM is scaffolded). However, the actual relationships between ToM sophistication and either brain volume (a proxy for general cognitive capacity) or social group size (a proxy for social network complexity) are unclear. Here, we let 39 individuals sampled from seven non-human primate species (lemurs, macaques, mangabeys, orangutans, gorillas and chimpanzees) engage in simple dyadic games against artificial ToM players (via a familiar human caregiver). Using computational analyses of primates' choice sequences, we found that the probability of exhibiting a ToM-compatible learning style is mainly driven by species' brain volume (rather than by social group size). Moreover, primates' social cognitive sophistication culminates in a precursor form of ToM, which still falls short of human fully-developed ToM abilities. PMID:29112973

Reading wild minds: A computational assay of Theory of Mind sophistication across seven primate species.

PubMed

Devaine, Marie; San-Galli, Aurore; Trapanese, Cinzia; Bardino, Giulia; Hano, Christelle; Saint Jalme, Michel; Bouret, Sebastien; Masi, Shelly; Daunizeau, Jean

2017-11-01

Theory of Mind (ToM), i.e. the ability to understand others' mental states, endows humans with highly adaptive social skills such as teaching or deceiving. Candidate evolutionary explanations have been proposed for the unique sophistication of human ToM among primates. For example, the Machiavellian intelligence hypothesis states that the increasing complexity of social networks may have induced a demand for sophisticated ToM. This type of scenario ignores neurocognitive constraints that may eventually be crucial limiting factors for ToM evolution. In contradistinction, the cognitive scaffolding hypothesis asserts that a species' opportunity to develop sophisticated ToM is mostly determined by its general cognitive capacity (on which ToM is scaffolded). However, the actual relationships between ToM sophistication and either brain volume (a proxy for general cognitive capacity) or social group size (a proxy for social network complexity) are unclear. Here, we let 39 individuals sampled from seven non-human primate species (lemurs, macaques, mangabeys, orangutans, gorillas and chimpanzees) engage in simple dyadic games against artificial ToM players (via a familiar human caregiver). Using computational analyses of primates' choice sequences, we found that the probability of exhibiting a ToM-compatible learning style is mainly driven by species' brain volume (rather than by social group size). Moreover, primates' social cognitive sophistication culminates in a precursor form of ToM, which still falls short of human fully-developed ToM abilities.

Teaching with Transmedia

ERIC Educational Resources Information Center

Pence, Harry E.

2012-01-01

The media environment is currently being dramatically changed by social networking, mobile computing, augmented reality, and transmedia. Of these four, transmedia is probably the least familiar to most educators. Transmedia enhances a central story idea with a variety of media components that provide additional information, give increased…

Structural controllability of unidirectional bipartite networks

NASA Astrophysics Data System (ADS)

Nacher, Jose C.; Akutsu, Tatsuya

2013-04-01

The interactions between fundamental life molecules, people and social organisations build complex architectures that often result in undesired behaviours. Despite all of the advances made in our understanding of network structures over the past decade, similar progress has not been achieved in the controllability of real-world networks. In particular, an analytical framework to address the controllability of bipartite networks is still absent. Here, we present a dominating set (DS)-based approach to bipartite network controllability that identifies the topologies that are relatively easy to control with the minimum number of driver nodes. Our theoretical calculations, assisted by computer simulations and an evaluation of real-world networks offer a promising framework to control unidirectional bipartite networks. Our analysis should open a new approach to reverting the undesired behaviours in unidirectional bipartite networks at will.

Information Extraction from Large-Multi-Layer Social Networks

DTIC Science & Technology

2015-08-06

mization [4]. Methods that fall into this category include spec- tral algorithms, modularity methods, and methods that rely on statistical inference...Snijders and Chris Baerveldt, “A multilevel network study of the effects of delinquent behavior on friendship evolution,” Journal of mathematical sociol- ogy...1970. [10] Ulrike Luxburg, “A tutorial on spectral clustering,” Statistics and Computing, vol. 17, no. 4, pp. 395–416, Dec. 2007. [11] R. A. Fisher, “On

Actor-network Procedures: Modeling Multi-factor Authentication, Device Pairing, Social Interactions

DTIC Science & Technology

2011-08-29

unmodifiable properties of your body; or the capabilities that you cannot convey to others, such as your handwriting . An identity can thus be determined by...network, two principals with the same set of secrets but, say , different computational powers, can be distinguished by timing their responses. Or they... says that configurations are finite sets. Partially ordered multisets, or pomsets were introduced and extensively studied by Vaughan Pratt and his

Applications of Network Visualisation in Infectious Disease Management

DTIC Science & Technology

2006-12-01

White, D.R. P-Systems: a structural model for kinship studies. Connections. 24, 22–33. 2001. [13] White, D.R., Batagelj , V., and Mrvar , A. Analyzing...Infectious Disease Management 12 - 6 RTO-MP-IST-063 [14] De Nooy, W., Mrvar , A., and Batagelj , V. Exploratory social network analysis with Pajek...Workspace for the World-Wide Web, Proceedings of the ACM Human Factors in Computing Systems, pp, 111. 1996 . [7] Plaisant, C. Facilitating Data

The Complexity of Crime Network Data: A Case Study of Its Consequences for Crime Control and the Study of Networks

PubMed Central

Rostami, Amir; Mondani, Hernan

2015-01-01

The field of social network analysis has received increasing attention during the past decades and has been used to tackle a variety of research questions, from prevention of sexually transmitted diseases to humanitarian relief operations. In particular, social network analyses are becoming an important component in studies of criminal networks and in criminal intelligence analysis. At the same time, intelligence analyses and assessments have become a vital component of modern approaches in policing, with policy implications for crime prevention, especially in the fight against organized crime. In this study, we have a unique opportunity to examine one specific Swedish street gang with three different datasets. These datasets are the most common information sources in studies of criminal networks: intelligence, surveillance and co-offending data. We use the data sources to build networks, and compare them by computing distance, centrality, and clustering measures. This study shows the complexity factor by which different data sources about the same object of study have a fundamental impact on the results. The same individuals have different importance ranking depending on the dataset and measure. Consequently, the data source plays a vital role in grasping the complexity of the phenomenon under study. Researchers, policy makers, and practitioners should therefore pay greater attention to the biases affecting the sources of the analysis, and be cautious when drawing conclusions based on intelligence assessments and limited network data. This study contributes to strengthening social network analysis as a reliable tool for understanding and analyzing criminality and criminal networks. PMID:25775130

The complexity of crime network data: a case study of its consequences for crime control and the study of networks.

PubMed

Rostami, Amir; Mondani, Hernan

2015-01-01

The field of social network analysis has received increasing attention during the past decades and has been used to tackle a variety of research questions, from prevention of sexually transmitted diseases to humanitarian relief operations. In particular, social network analyses are becoming an important component in studies of criminal networks and in criminal intelligence analysis. At the same time, intelligence analyses and assessments have become a vital component of modern approaches in policing, with policy implications for crime prevention, especially in the fight against organized crime. In this study, we have a unique opportunity to examine one specific Swedish street gang with three different datasets. These datasets are the most common information sources in studies of criminal networks: intelligence, surveillance and co-offending data. We use the data sources to build networks, and compare them by computing distance, centrality, and clustering measures. This study shows the complexity factor by which different data sources about the same object of study have a fundamental impact on the results. The same individuals have different importance ranking depending on the dataset and measure. Consequently, the data source plays a vital role in grasping the complexity of the phenomenon under study. Researchers, policy makers, and practitioners should therefore pay greater attention to the biases affecting the sources of the analysis, and be cautious when drawing conclusions based on intelligence assessments and limited network data. This study contributes to strengthening social network analysis as a reliable tool for understanding and analyzing criminality and criminal networks.

On the design of neuro-controllers for individual and social learning behaviour in autonomous robots: an evolutionary approach

NASA Astrophysics Data System (ADS)

Pini, Giovanni; Tuci, Elio

2008-06-01

In biology/psychology, the capability of natural organisms to learn from the observation/interaction with conspecifics is referred to as social learning. Roboticists have recently developed an interest in social learning, since it might represent an effective strategy to enhance the adaptivity of a team of autonomous robots. In this study, we show that a methodological approach based on artifcial neural networks shaped by evolutionary computation techniques can be successfully employed to synthesise the individual and social learning mechanisms for robots required to learn a desired action (i.e. phototaxis or antiphototaxis).

Overview of Aro Program on Network Science for Human Decision Making

NASA Astrophysics Data System (ADS)

West, Bruce J.

This program brings together researchers from disparate disciplines to work on a complex research problem that defies confinement within any single discipline. Consequently, not only are new and rewarding solutions sought and obtained for a problem of importance to society and the Army, that is, the human dimension of complex networks, but, in addition, collaborations are established that would not otherwise have formed given the traditional disciplinary compartmentalization of research. This program develops the basic research foundation of a science of networks supporting the linkage between the physical and human (cognitive and social) domains as they relate to human decision making. The strategy is to extend the recent methods of non-equilibrium statistical physics to non-stationary, renewal stochastic processes that appear to be characteristic of the interactions among nodes in complex networks. We also pursue understanding of the phenomenon of synchronization, whose mathematical formulation has recently provided insight into how complex networks reach accommodation and cooperation. The theoretical analyses of complex networks, although mathematically rigorous, often elude analytic solutions and require computer simulation and computation to analyze the underlying dynamic process.

Networks In Real Space: Characteristics and Analysis for Biology and Mechanics

NASA Astrophysics Data System (ADS)

Modes, Carl; Magnasco, Marcelo; Katifori, Eleni

Functional networks embedded in physical space play a crucial role in countless biological and physical systems, from the efficient dissemination of oxygen, blood sugars, and hormonal signals in vascular systems to the complex relaying of informational signals in the brain to the distribution of stress and strain in architecture or static sand piles. Unlike their more-studied abstract cousins, such as the hyperlinked internet, social networks, or economic and financial connections, these networks are both constrained by and intimately connected to the physicality of their real, embedding space. We report on the results of new computational and analytic approaches tailored to these physical networks with particular implications and insights for mammalian organ vasculature.

Calibrating Bayesian Network Representations of Social-Behavioral Models

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

Whitney, Paul D.; Walsh, Stephen J.

2010-04-08

While human behavior has long been studied, recent and ongoing advances in computational modeling present opportunities for recasting research outcomes in human behavior. In this paper we describe how Bayesian networks can represent outcomes of human behavior research. We demonstrate a Bayesian network that represents political radicalization research – and show a corresponding visual representation of aspects of this research outcome. Since Bayesian networks can be quantitatively compared with external observations, the representation can also be used for empirical assessments of the research which the network summarizes. For a political radicalization model based on published research, we show this empiricalmore » comparison with data taken from the Minorities at Risk Organizational Behaviors database.« less

Finding Correlation between Protein Protein Interaction Modules Using Semantic Web Techniques

NASA Astrophysics Data System (ADS)

Kargar, Mehdi; Moaven, Shahrouz; Abolhassani, Hassan

Many complex networks such as social networks and computer show modular structures, where edges between nodes are much denser within modules than between modules. It is strongly believed that cellular networks are also modular, reflecting the relative independence and coherence of different functional units in a cell. In this paper we used a human curated dataset. In this paper we consider each module in the PPI network as ontology. Using techniques in ontology alignment, we compare each pair of modules in the network. We want to see that is there a correlation between the structure of each module or they have totally different structures. Our results show that there is no correlation between proteins in a protein protein interaction network.

Communication and collaboration technologies.

PubMed

Cheeseman, Susan E

2012-01-01

This is the third in a series of columns exploring health information technology (HIT) in the neonatal intensive care unit (NICU). The first column provided background information on the implementation of information technology throughout the health care delivery system, as well as the requisite informatics competencies needed for nurses to fully engage in the digital era of health care. The second column focused on information and resources to master basic computer competencies described by the TIGER initiative (Technology Informatics Guiding Education Reform) as learning about computers, computer networks, and the transfer of data.1 This column will provide additional information related to basic computer competencies, focusing on communication and collaboration technologies. Computers and the Internet have transformed the way we communicate and collaborate. Electronic communication is the ability to exchange information through the use of computer equipment and software.2 Broadly defined, any technology that facilitates linking one or more individuals together is a collaborative tool. Collaboration using technology encompasses an extensive range of applications that enable groups of individuals to work together including e-mail, instant messaging (IM ), and several web applications collectively referred to as Web 2.0 technologies. The term Web 2.0 refers to web applications where users interact and collaborate with each other in a collective exchange of ideas generating content in a virtual community. Examples of Web 2.0 technologies include social networking sites, blogs, wikis, video sharing sites, and mashups. Many organizations are developing collaborative strategies and tools for employees to connect and interact using web-based social media technologies.3.

Epidemics and dimensionality in hierarchical networks

NASA Astrophysics Data System (ADS)

Zheng, Da-Fang; Hui, P. M.; Trimper, Steffen; Zheng, Bo

2005-07-01

Epidemiological processes are studied within a recently proposed hierarchical network model using the susceptible-infected-refractory dynamics of an epidemic. Within the network model, a population may be characterized by H independent hierarchies or dimensions, each of which consists of groupings of individuals into layers of subgroups. Detailed numerical simulations reveal that for H>1, global spreading results regardless of the degree of homophily of the individuals forming a social circle. For H=1, a transition from global to 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).

Do regions of ALICE matter? Social relationships and data exchanges in the Grid

NASA Astrophysics Data System (ADS)

Widmer, E. D.; Carminati, F.; Grigoras, C.; Viry, G.; Galli Carminati, G.

2012-06-01

Following a previous publication [1], this study aims at investigating the impact of regional affiliations of centres on the organisation of collaboration within the Distributed Computing ALICE infrastructure, based on social networks methods. A self-administered questionnaire was sent to all centre managers about support, email interactions and wished collaborations in the infrastructure. Several additional measures, stemming from technical observations were produced, such as bandwidth, data transfers and Internet Round Trip Time (RTT) were also included. Information for 50 centres were considered (60% response rate). Empirical analysis shows that despite the centralisation on CERN, the network is highly organised by regions. The results are discussed in the light of policy and efficiency issues.

Do regions matter in ALICE?. Social relationships and data exchanges in the Grid

NASA Astrophysics Data System (ADS)

Widmer, E. D.; Viry, G.; Carminati, F.; Galli-Carminati, G.

2012-02-01

This study aims at investigating the impact of regional affiliations of centres on the organisation of collaborations within the Distributed Computing ALICE infrastructure, based on social networks methods. A self-administered questionnaire was sent to all centre managers about support, email interactions and wished collaborations in the infrastructure. Several additional measures, stemming from technical observations were collected, such as bandwidth, data transfers and Internet Round Trip Time (RTT) were also included. Information for 50 centres were considered (about 70% response rate). Empirical analysis shows that despite the centralisation on CERN, the network is highly organised by regions. The results are discussed in the light of policy and efficiency issues.

Mobile Network Data for Public Health: Opportunities and Challenges

PubMed Central

Oliver, Nuria; Matic, Aleksandar; Frias-Martinez, Enrique

2015-01-01

The ubiquity of mobile phones worldwide is generating an unprecedented amount of human behavioral data both at an individual and aggregated levels. The study of this data as a rich source of information about human behavior emerged almost a decade ago. Since then, it has grown into a fertile area of research named computational social sciences with a wide variety of applications in different fields such as social networks, urban and transport planning, economic development, emergency relief, and, recently, public health. In this paper, we briefly describe the state of the art on using mobile phone data for public health, and present the opportunities and challenges that this kind of data presents for public health. PMID:26301211

Sharing Digital Data

ERIC Educational Resources Information Center

Benedis-Grab, Gregory

2011-01-01

Computers have changed the landscape of scientific research in profound ways. Technology has always played an important role in scientific experimentation--through the development of increasingly sophisticated tools, the measurement of elusive quantities, and the processing of large amounts of data. However, the advent of social networking and the…

Future Operating Concept - Joint Computer Network Operations

DTIC Science & Technology

2010-02-17

into the system‖ at a later date by circumventing the need for access privileges 3. Cyberwarfare Tools 67 • Social Engineering • Hacking ...Forces Command, Joint Operating Environment 2008, November 2008https://us.jfcom.mil/sites/ J5 /j59/default.aspx VIII Appendix V

219. Changes in Functional Networks Underlying Social Cognition Following Cognitive Training in Individuals at Risk for Psychosis

PubMed Central

Haut, Kristen; Saxena, Abhishek; Yin, Hong; Carol, Emily; Dodell-Feder, David; Lincoln, Sarah Hope; Tully, Laura; Keshavan, Matcheri; Seidman, Larry J.; Nahum, Mor; Hooker, Christine

2017-01-01

Abstract Background: Deficits in social cognition are prominent features of schizophrenia that play a large role in functional impairments and disability. Performance deficits in these domains are associated with altered activity in functional networks, including those that support social cognitive abilities such as emotion recognition. These social cognitive deficits and alterations in neural networks are present prior to the onset of frank psychotic symptoms and thus present a potential target for intervention in early phases of the illness, including in individuals at clinical high risk (CHR) for psychosis. This study assessed changes in social cognitive functional networks following targeted cognitive training (TCT) in CHR individuals. Methods: 14 CHR subjects (7 male, mean age = 21.9) showing attenuated psychotic symptoms as assessed by the SIPS were included in the study. Subjects underwent a clinical evaluation and a functional MRI session prior to and subsequent to completing 40 hours (8 weeks) of targeted cognitive and social cognitive training using Lumosity and SocialVille. 14 matched healthy control (HC) subjects also underwent a single fMRI session as a comparison group for functional activity. Resting state fMRI was acquired as well as fMRI during performance of an emotion recognition task. Group level differences in BOLD activity between HC and CHR group before TCT, and CHR group before and after TCT were computed. Changes in social cognitive network functional connectivity at rest and during task performance was evaluated using seed-based connectivity analyses and psychophysiological interaction (PPI). Results: Prior to training, CHR individuals demonstrated hyperactivity in the amygdala, posterior cingulate, and superior temporal sulcus (STS) during emotion recognition, suggesting inefficient processing. This hyperactivity normalized somewhat after training, with CHR individuals showing less hyperactivity in the amygdala in response to emotional faces. In addition, training was associated with increased connectivity in emotion processing networks, including greater STS-medial prefrontal connectivity and normalization of amygdala connectivity patterns. Conclusion: These results suggest that targeted cognitive training produced improvements in emotion recognition and may be effective in altering functional network connectivity in networks associated with psychosis risk. TCT may be a useful tool for early intervention in individuals at risk for psychotic disorders to address behaviors that impact functional outcome.

Methods for extracting social network data from chatroom logs

NASA Astrophysics Data System (ADS)

Osesina, O. Isaac; McIntire, John P.; Havig, Paul R.; Geiselman, Eric E.; Bartley, Cecilia; Tudoreanu, M. Eduard

2012-06-01

Identifying social network (SN) links within computer-mediated communication platforms without explicit relations among users poses challenges to researchers. Our research aims to extract SN links in internet chat with multiple users engaging in synchronous overlapping conversations all displayed in a single stream. We approached this problem using three methods which build on previous research. Response-time analysis builds on temporal proximity of chat messages; word context usage builds on keywords analysis and direct addressing which infers links by identifying the intended message recipient from the screen name (nickname) referenced in the message [1]. Our analysis of word usage within the chat stream also provides contexts for the extracted SN links. To test the capability of our methods, we used publicly available data from Internet Relay Chat (IRC), a real-time computer-mediated communication (CMC) tool used by millions of people around the world. The extraction performances of individual methods and their hybrids were assessed relative to a ground truth (determined a priori via manual scoring).

NDEx: A Community Resource for Sharing and Publishing of Biological Networks.

PubMed

Pillich, Rudolf T; Chen, Jing; Rynkov, Vladimir; Welker, David; Pratt, Dexter

2017-01-01

Networks are a powerful and flexible paradigm that facilitate communication and computation about interactions of any type, whether social, economic, or biological. NDEx, the Network Data Exchange, is an online commons to enable new modes of collaboration and publication using biological networks. NDEx creates an access point and interface to a broad range of networks, whether they express molecular interactions, curated relationships from literature, or the outputs of systematic analysis of big data. Research organizations can use NDEx as a distribution channel for networks they generate or curate. Developers of bioinformatic applications can store and query NDEx networks via a common programmatic interface. NDEx can also facilitate the integration of networks as data in electronic publications, thus making a step toward an ecosystem in which networks bearing data, hypotheses, and findings flow seamlessly between scientists.

The Social Network of Tracer Variations and O(100) Uncertain Photochemical Parameters in the Community Atmosphere Model

NASA Astrophysics Data System (ADS)

Lucas, D. D.; Labute, M.; Chowdhary, K.; Debusschere, B.; Cameron-Smith, P. J.

2014-12-01

Simulating the atmospheric cycles of ozone, methane, and other radiatively important trace gases in global climate models is computationally demanding and requires the use of 100's of photochemical parameters with uncertain values. Quantitative analysis of the effects of these uncertainties on tracer distributions, radiative forcing, and other model responses is hindered by the "curse of dimensionality." We describe efforts to overcome this curse using ensemble simulations and advanced statistical methods. Uncertainties from 95 photochemical parameters in the trop-MOZART scheme were sampled using a Monte Carlo method and propagated through 10,000 simulations of the single column version of the Community Atmosphere Model (CAM). The variance of the ensemble was represented as a network with nodes and edges, and the topology and connections in the network were analyzed using lasso regression, Bayesian compressive sensing, and centrality measures from the field of social network theory. Despite the limited sample size for this high dimensional problem, our methods determined the key sources of variation and co-variation in the ensemble and identified important clusters in the network topology. Our results can be used to better understand the flow of photochemical uncertainty in simulations using CAM and other climate models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and supported by the DOE Office of Science through the Scientific Discovery Through Advanced Computing (SciDAC).

Text Messaging and Social Network Site Use to Facilitate Alcohol Involvement: A Comparison of U.S. and Korean College Students.

PubMed

Jensen, Michaeline; Hussong, Andrea M; Baik, Jeesook

2018-05-01

Alcohol-related content on public social networking sites (SNS) has been linked to collegiate alcohol use, but we know little about whether and how private forms of computer-mediated communication (CMC), like text messaging, are related to collegiate drinking, nor how alcohol-related CMC content and drinking are associated in non-Western cultures. We examined the ways in which private text messaging and SNS are used to facilitate alcohol involvement among U.S. (n = 575) and Korean (n = 462) college students (total N = 1037), two technologically wired cultures with prevalent collegiate alcohol misuse. Results show that college students prefer private text messaging over SNS to find parties and facilitate alcohol involvement, and this preference tends to be stronger in Korea than the United States. Private text messaging is more consistently and strongly associated with alcohol use frequency and heavy episodic drinking than SNS posts in both countries, with particularly robust associations between private text messaging and drinking in the United States. Findings underscore the role of CMCs in facilitating alcohol involvement and highlight the potential for analysis of private message content to further understand computer-mediated social processes in college student drinking.

The relationship between social desirability bias and self-reports of health, substance use, and social network factors among urban substance users in Baltimore, Maryland.

PubMed

Latkin, Carl A; Edwards, Catie; Davey-Rothwell, Melissa A; Tobin, Karin E

2017-10-01

Social desirability response bias may lead to inaccurate self-reports and erroneous study conclusions. The present study examined the relationship between social desirability response bias and self-reports of mental health, substance use, and social network factors among a community sample of inner-city substance users. The study was conducted in a sample of 591 opiate and cocaine users in Baltimore, Maryland from 2009 to 2013. Modified items from the Marlowe-Crowne Social Desirability Scale were included in the survey, which was conducted face-to-face and using Audio Computer Self Administering Interview (ACASI) methods. There were highly statistically significant differences in levels of social desirability response bias by levels of depressive symptoms, drug use stigma, physical health status, recent opiate and cocaine use, Alcohol Use Disorders Identification Test (AUDIT) scores, and size of social networks. There were no associations between health service utilization measures and social desirability bias. In multiple logistic regression models, even after including the Center for Epidemiologic Studies Depression Scale (CES-D) as a measure of depressive symptomology, social desirability bias was associated with recent drug use and drug user stigma. Social desirability bias was not associated with enrollment in prior research studies. These findings suggest that social desirability bias is associated with key health measures and that the associations are not primarily due to depressive symptoms. Methods are needed to reduce social desirability bias. Such methods may include the wording and prefacing of questions, clearly defining the role of "study participant," and assessing and addressing motivations for socially desirable responses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Integration of individual and social information for decision-making in groups of different sizes.

PubMed

Park, Seongmin A; Goïame, Sidney; O'Connor, David A; Dreher, Jean-Claude

2017-06-01

When making judgments in a group, individuals often revise their initial beliefs about the best judgment to make given what others believe. Despite the ubiquity of this phenomenon, we know little about how the brain updates beliefs when integrating personal judgments (individual information) with those of others (social information). Here, we investigated the neurocomputational mechanisms of how we adapt our judgments to those made by groups of different sizes, in the context of jury decisions for a criminal. By testing different theoretical models, we showed that a social Bayesian inference model captured changes in judgments better than 2 other models. Our results showed that participants updated their beliefs by appropriately weighting individual and social sources of information according to their respective credibility. When investigating 2 fundamental computations of Bayesian inference, belief updates and credibility estimates of social information, we found that the dorsal anterior cingulate cortex (dACC) computed the level of belief updates, while the bilateral frontopolar cortex (FPC) was more engaged in individuals who assigned a greater credibility to the judgments of a larger group. Moreover, increased functional connectivity between these 2 brain regions reflected a greater influence of group size on the relative credibility of social information. These results provide a mechanistic understanding of the computational roles of the FPC-dACC network in steering judgment adaptation to a group's opinion. Taken together, these findings provide a computational account of how the human brain integrates individual and social information for decision-making in groups.

Propagation and immunization of infection on general networks with both homogeneous and heterogeneous components

NASA Astrophysics Data System (ADS)

Liu, Zonghua; Lai, Ying-Cheng; Ye, Nong

2003-03-01

We consider the entire spectrum of architectures of general networks, ranging from being heterogeneous (scale-free) to homogeneous (random), and investigate the infection dynamics by using a three-state epidemiological model that does not involve the mechanism of self-recovery. This model is relevant to realistic situations such as the propagation of a flu virus or information over a social network. Our heuristic analysis and computations indicate that (1) regardless of the network architecture, there exists a substantial fraction of nodes that can never be infected and (2) heterogeneous networks are relatively more robust against spreads of infection as compared with homogeneous networks. We have also considered the problem of immunization for preventing wide spread of infection, with the result that targeted immunization is effective for heterogeneous networks.

Blackmail propagation on small-world networks

NASA Astrophysics Data System (ADS)

Shao, Zhi-Gang; Jian-Ping Sang; Zou, Xian-Wu; Tan, Zhi-Jie; Jin, Zhun-Zhi

2005-06-01

The dynamics of the blackmail propagation model based on small-world networks is investigated. It is found that for a given transmitting probability λ the dynamical behavior of blackmail propagation transits from linear growth type to logistical growth one with the network randomness p increases. The transition takes place at the critical network randomness pc=1/N, where N is the total number of nodes in the network. For a given network randomness p the dynamical behavior of blackmail propagation transits from exponential decrease type to logistical growth one with the transmitting probability λ increases. The transition occurs at the critical transmitting probability λc=1/, where is the average number of the nearest neighbors. The present work will be useful for understanding computer virus epidemics and other spreading phenomena on communication and social networks.

An Online Algorithm for Maximizing Submodular Functions

DTIC Science & Technology

2007-12-20

dynamics of the social network are known. In theory, our online algorithms could be used to adapt a marketing campaign to unknown or time-varying social...An Online Algorithm for Maximizing Submodular Functions Matthew Streeter Daniel Golovin December 20, 2007 CMU-CS-07-171 School of Computer Science...number. 1. REPORT DATE 20 DEC 2007 2. REPORT TYPE 3. DATES COVERED 00-00-2007 to 00-00-2007 4. TITLE AND SUBTITLE An Online Algorithm for

Social percolation models

NASA Astrophysics Data System (ADS)

Solomon, Sorin; Weisbuch, Gerard; de Arcangelis, Lucilla; Jan, Naeem; Stauffer, Dietrich

2000-03-01

We here relate the occurrence of extreme market shares, close to either 0 or 100%, in the media industry to a percolation phenomenon across the social network of customers. We further discuss the possibility of observing self-organized criticality when customers and cinema producers adjust their preferences and the quality of the produced films according to previous experience. Comprehensive computer simulations on square lattices do indeed exhibit self-organized criticality towards the usual percolation threshold and related scaling behaviour.

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.

Interplay between cooperation-enhancing mechanisms in evolutionary games with tag-mediated interactions

NASA Astrophysics Data System (ADS)

Hadzibeganovic, Tarik; Stauffer, Dietrich; Han, Xiao-Pu

2018-04-01

Cooperation is fundamental for the long-term survival of biological, social, and technological networks. Previously, mechanisms for the enhancement of cooperation, such as network reciprocity, have largely been studied in isolation and with often inconclusive findings. Here, we present an evolutionary, multiagent-based, and spatially explicit computer model to specifically address the interactive interplay between such mechanisms. We systematically investigate the effects of phenotypic diversity, network structure, and rewards on cooperative behavior emerging in a population of reproducing artificial decision makers playing tag-mediated evolutionary games. Cooperative interactions are rewarded such that both the benefits of recipients and costs of donators are affected by the reward size. The reward size is determined by the number of cooperative acts occurring within a given reward time frame. Our computational experiments reveal that small reward frames promote unconditional cooperation in populations with both low and high diversity, whereas large reward frames lead to cycles of conditional and unconditional strategies at high but not at low diversity. Moreover, an interaction between rewards and spatial structure shows that relative to small reward frames, there is a strong difference between the frequency of conditional cooperators populating rewired versus non-rewired networks when the reward frame is large. Notably, in a less diverse population, the total number of defections is comparable across different network topologies, whereas in more diverse environments defections become more frequent in a regularly structured than in a rewired, small-world network of contacts. Acknowledging the importance of such interaction effects in social dilemmas will have inevitable consequences for the future design of cooperation-enhancing protocols in large-scale, distributed, and decentralized systems such as peer-to-peer networks.

CyberPsychological Computation on Social Community of Ubiquitous Learning.

PubMed

Zhou, Xuan; Dai, Genghui; Huang, Shuang; Sun, Xuemin; Hu, Feng; Hu, Hongzhi; Ivanović, Mirjana

2015-01-01

Under the modern network environment, ubiquitous learning has been a popular way for people to study knowledge, exchange ideas, and share skills in the cyberspace. Existing research findings indicate that the learners' initiative and community cohesion play vital roles in the social communities of ubiquitous learning, and therefore how to stimulate the learners' interest and participation willingness so as to improve their enjoyable experiences in the learning process should be the primary consideration on this issue. This paper aims to explore an effective method to monitor the learners' psychological reactions based on their behavioral features in cyberspace and therefore provide useful references for adjusting the strategies in the learning process. In doing so, this paper firstly analyzes the psychological assessment of the learners' situations as well as their typical behavioral patterns and then discusses the relationship between the learners' psychological reactions and their observable features in cyberspace. Finally, this paper puts forward a CyberPsychological computation method to estimate the learners' psychological states online. Considering the diversity of learners' habitual behaviors in the reactions to their psychological changes, a BP-GA neural network is proposed for the computation based on their personalized behavioral patterns.

CyberPsychological Computation on Social Community of Ubiquitous Learning

PubMed Central

Zhou, Xuan; Dai, Genghui; Huang, Shuang; Sun, Xuemin; Hu, Feng; Hu, Hongzhi; Ivanović, Mirjana

2015-01-01

Under the modern network environment, ubiquitous learning has been a popular way for people to study knowledge, exchange ideas, and share skills in the cyberspace. Existing research findings indicate that the learners' initiative and community cohesion play vital roles in the social communities of ubiquitous learning, and therefore how to stimulate the learners' interest and participation willingness so as to improve their enjoyable experiences in the learning process should be the primary consideration on this issue. This paper aims to explore an effective method to monitor the learners' psychological reactions based on their behavioral features in cyberspace and therefore provide useful references for adjusting the strategies in the learning process. In doing so, this paper firstly analyzes the psychological assessment of the learners' situations as well as their typical behavioral patterns and then discusses the relationship between the learners' psychological reactions and their observable features in cyberspace. Finally, this paper puts forward a CyberPsychological computation method to estimate the learners' psychological states online. Considering the diversity of learners' habitual behaviors in the reactions to their psychological changes, a BP-GA neural network is proposed for the computation based on their personalized behavioral patterns. PMID:26557846

The Society of Brains: How Alan Turing and Marvin Minsky Were Both Right

NASA Astrophysics Data System (ADS)

Struzik, Zbigniew R.

2015-04-01

In his well-known prediction, Alan Turing stated that computer intelligence would surpass human intelligence by the year 2000. Although the Turing Test, as it became known, was devised to be played by one human against one computer, this is not a fair setup. Every human is a part of a social network, and a fairer comparison would be a contest between one human at the console and a network of computers behind the console. Around the year 2000, the number of web pages on the WWW overtook the number of neurons in the human brain. But these websites would be of little use without the ability to search for knowledge. By the year 2000 Google Inc. had become the search engine of choice, and the WWW became an intelligent entity. This was not without good reason. The basis for the search engine was the analysis of the ’network of knowledge’. The PageRank algorithm, linking information on the web according to the hierarchy of ‘link popularity’, continues to provide the basis for all of Google's web search tools. While PageRank was developed by Larry Page and Sergey Brin in 1996 as part of a research project about a new kind of search engine, PageRank is in its essence the key to representing and using static knowledge in an emergent intelligent system. Here I argue that Alan Turing was right, as hybrid human-computer internet machines have already surpassed our individual intelligence - this was done around the year 2000 by the Internet - the socially-minded, human-computer hybrid Homo computabilis-socialis. Ironically, the Internet's intelligence also emerged to a large extent from ‘exploiting’ humans - the key to the emergence of machine intelligence has been discussed by Marvin Minsky in his work on the foundations of intelligence through interacting agents’ knowledge. As a consequence, a decade and a half decade into the 21st century, we appear to be much better equipped to tackle the problem of the social origins of humanity - in particular thanks to the power of the intelligent partner-in-the-quest machine, however, we should not wait too long...

Social network analysis of the genetic structure of Pacific islanders.

PubMed

Terrell, John Edward

2010-05-01

Social network analysis (SNA) is a body of theory and a set of relatively new computer-aided techniques used in the analysis and study of relational data. Recent studies of autosomal markers from over 40 human populations in the south-western Pacific have further documented the remarkable degree of genetic diversity in this part of the world. I report additional analysis using SNA methods contributing new controlled observations on the structuring of genetic diversity among these islanders. These SNA mappings are then compared with model-based network expectations derived from the geographic distances among the same populations. Previous studies found that genetic divergence among island Melanesian populations is organised by island, island size/topography, and position (coastal vs. inland), and that similarities observed correlate only weakly with an isolation-by-distance model. Using SNA methods, however, improves the resolution of among population comparison, and suggests that isolation by distance constrained by social networks together with position (coastal/inland) accounts for much of the population structuring observed. The multilocus data now available is also in accord with current thinking on the impact of major biogeographical transformations on prehistoric colonisation and post-settlement human interaction in Oceania.

Transmission of severe acute respiratory syndrome in dynamical small-world networks

NASA Astrophysics Data System (ADS)

Masuda, Naoki; Konno, Norio; Aihara, Kazuyuki

2004-03-01

The outbreak of severe acute respiratory syndrome (SARS) is still threatening the world because of a possible resurgence. In the current situation that effective medical treatments such as antiviral drugs are not discovered yet, dynamical features of the epidemics should be clarified for establishing strategies for tracing, quarantine, isolation, and regulating social behavior of the public at appropriate costs. Here we propose a network model for SARS epidemics and discuss why superspreaders emerged and why SARS spread especially in hospitals, which were key factors of the recent outbreak. We suggest that superspreaders are biologically contagious patients, and they may amplify the spreads by going to potentially contagious places such as hospitals. To avoid mass transmission in hospitals, it may be a good measure to treat suspected cases without hospitalizing them. Finally, we indicate that SARS probably propagates in small-world networks associated with human contacts and that the biological nature of individuals and social group properties are factors more important than the heterogeneous rates of social contacts among individuals. This is in marked contrast with epidemics of sexually transmitted diseases or computer viruses to which scale-free network models often apply.

Measure of robustness for complex networks

NASA Astrophysics Data System (ADS)

Youssef, Mina Nabil

Critical infrastructures are repeatedly attacked by external triggers causing tremendous amount of damages. Any infrastructure can be studied using the powerful theory of complex networks. A complex network is composed of extremely large number of different elements that exchange commodities providing significant services. The main functions of complex networks can be damaged by different types of attacks and failures that degrade the network performance. These attacks and failures are considered as disturbing dynamics, such as the spread of viruses in computer networks, the spread of epidemics in social networks, and the cascading failures in power grids. Depending on the network structure and the attack strength, every network differently suffers damages and performance degradation. Hence, quantifying the robustness of complex networks becomes an essential task. In this dissertation, new metrics are introduced to measure the robustness of technological and social networks with respect to the spread of epidemics, and the robustness of power grids with respect to cascading failures. First, we introduce a new metric called the Viral Conductance (VCSIS ) to assess the robustness of networks with respect to the spread of epidemics that are modeled through the susceptible/infected/susceptible (SIS) epidemic approach. In contrast to assessing the robustness of networks based on a classical metric, the epidemic threshold, the new metric integrates the fraction of infected nodes at steady state for all possible effective infection strengths. Through examples, VCSIS provides more insights about the robustness of networks than the epidemic threshold. In addition, both the paradoxical robustness of Barabasi-Albert preferential attachment networks and the effect of the topology on the steady state infection are studied, to show the importance of quantifying the robustness of networks. Second, a new metric VCSIR is introduced to assess the robustness of networks with respect to the spread of susceptible/infected/recovered (SIR) epidemics. To compute VCSIR, we propose a novel individual-based approach to model the spread of SIR epidemics in networks, which captures the infection size for a given effective infection rate. Thus, VCSIR quantitatively integrates the infection strength with the corresponding infection size. To optimize the VCSIR metric, a new mitigation strategy is proposed, based on a temporary reduction of contacts in social networks. The social contact network is modeled as a weighted graph that describes the frequency of contacts among the individuals. Thus, we consider the spread of an epidemic as a dynamical system, and the total number of infection cases as the state of the system, while the weight reduction in the social network is the controller variable leading to slow/reduce the spread of epidemics. Using optimal control theory, the obtained solution represents an optimal adaptive weighted network defined over a finite time interval. Moreover, given the high complexity of the optimization problem, we propose two heuristics to find the near optimal solutions by reducing the contacts among the individuals in a decentralized way. Finally, the cascading failures that can take place in power grids and have recently caused several blackouts are studied. We propose a new metric to assess the robustness of the power grid with respect to the cascading failures. The power grid topology is modeled as a network, which consists of nodes and links representing power substations and transmission lines, respectively. We also propose an optimal islanding strategy to protect the power grid when a cascading failure event takes place in the grid. The robustness metrics are numerically evaluated using real and synthetic networks to quantify their robustness with respect to disturbing dynamics. We show that the proposed metrics outperform the classical metrics in quantifying the robustness of networks and the efficiency of the mitigation strategies. In summary, our work advances the network science field in assessing the robustness of complex networks with respect to various disturbing dynamics.

Network Penetration Testing and Research

NASA Technical Reports Server (NTRS)

Murphy, Brandon F.

2013-01-01

This paper will focus the on research and testing done on penetrating a network for security purposes. This research will provide the IT security office new methods of attacks across and against a company's network as well as introduce them to new platforms and software that can be used to better assist with protecting against such attacks. Throughout this paper testing and research has been done on two different Linux based operating systems, for attacking and compromising a Windows based host computer. Backtrack 5 and BlackBuntu (Linux based penetration testing operating systems) are two different "attacker'' computers that will attempt to plant viruses and or NASA USRP - Internship Final Report exploits on a host Windows 7 operating system, as well as try to retrieve information from the host. On each Linux OS (Backtrack 5 and BlackBuntu) there is penetration testing software which provides the necessary tools to create exploits that can compromise a windows system as well as other operating systems. This paper will focus on two main methods of deploying exploits 1 onto a host computer in order to retrieve information from a compromised system. One method of deployment for an exploit that was tested is known as a "social engineering" exploit. This type of method requires interaction from unsuspecting user. With this user interaction, a deployed exploit may allow a malicious user to gain access to the unsuspecting user's computer as well as the network that such computer is connected to. Due to more advance security setting and antivirus protection and detection, this method is easily identified and defended against. The second method of exploit deployment is the method mainly focused upon within this paper. This method required extensive research on the best way to compromise a security enabled protected network. Once a network has been compromised, then any and all devices connected to such network has the potential to be compromised as well. With a compromised network, computers and devices can be penetrated through deployed exploits. This paper will illustrate the research done to test ability to penetrate a network without user interaction, in order to retrieve personal information from a targeted host.

Sticking with the nice guy: trait warmth information impairs learning and modulates person perception brain network activity.

PubMed

Lee, Victoria K; Harris, Lasana T

2014-12-01

Social learning requires inferring social information about another person, as well as evaluating outcomes. Previous research shows that prior social information biases decision making and reduces reliance on striatal activity during learning (Delgado, Frank, & Phelps, Nature Neuroscience 8 (11): 1611-1618, 2005). A rich literature in social psychology on person perception demonstrates that people spontaneously infer social information when viewing another person (Fiske & Taylor, 2013) and engage a network of brain regions, including the medial prefrontal cortex, temporal parietal junction, superior temporal sulcus, and precuneus (Amodio & Frith, Nature Reviews Neuroscience, 7(4), 268-277, 2006; Haxby, Gobbini, & Montgomery, 2004; van Overwalle Human Brain Mapping, 30, 829-858, 2009). We investigate the role of these brain regions during social learning about well-established dimensions of person perception-trait warmth and trait competence. We test the hypothesis that activity in person perception brain regions interacts with learning structures during social learning. Participants play an investment game where they must choose an agent to invest on their behalf. This choice is guided by cues signaling trait warmth or trait competence based on framing of monetary returns. Trait warmth information impairs learning about human but not computer agents, while trait competence information produces similar learning rates for human and computer agents. We see increased activation to warmth information about human agents in person perception brain regions. Interestingly, activity in person perception brain regions during the decision phase negatively predicts activity in the striatum during feedback for trait competence inferences about humans. These results suggest that social learning may engage additional processing within person perception brain regions that hampers learning in economic contexts.

Vertical Interaction in Open Software Engineering Communities

DTIC Science & Technology

2009-03-01

Program in CASOS (NSF,DGE-9972762), the Office of Naval Research under Dynamic Network Analysis program (N00014-02-1-0973, the Air Force Office of...W91WAW07C0063) for research in the area of dynamic network analysis. Additional support was provided by CASOS - the center for Computational Analysis of Social...methods across the domain. For a given project, de - velopers can choose from dozens of models, tools, platforms, and languages for specification, design

Towards a C2 Poly-Visualization Tool: Leveraging the Power of Social-Network Analysis and GIS

DTIC Science & Technology

2011-06-01

from Magsino.14 AutoMap, a product of CASOS at Carnegie Mellon University, is a text-mining tool that enables the extraction of network data from...enables community leaders to prepare for biological attacks using computational models. BioWar is a CASOS package that combines many factors into a...models, demographically accurate agent modes, wind dispersion models, and an error-diagnostic model. Construct, also developed by CASOS , is a

Social Network Theory in Engineering Education

NASA Astrophysics Data System (ADS)

Simon, Peter A.

Collaborative groups are important both in the learning environment of engineering education and, in the real world, the business of engineering design. Selecting appropriate individuals to form an effective group and monitoring a group's progress are important aspects of successful task performance. This exploratory study looked at using the concepts of cognitive social structures, structural balance, and centrality from social network analysis as well as the measures of emotional intelligence. The concepts were used to analyze potential team members to examine if an individual's ability to perceive emotion in others and the self and to use, understand, and manage those emotions are a factor in a group's performance. The students from a capstone design course in computer engineering were used as volunteer subjects. They were formed into groups and assigned a design exercise to determine whether and which of the above-mentioned tools would be effective in both selecting teams and predicting the quality of the resultant design. The results were inconclusive with the exception of an individual's ability to accurately perceive emotions. The instruments that were successful were the Self-Monitoring scale and the accuracy scores derived from cognitive social structures and Level IV of network levels of analysis.

Analysis of Context Dependence in Social Interaction Networks of a Massively Multiplayer Online Role-Playing Game

PubMed Central

Son, Seokshin; Kang, Ah Reum; Kim, Hyun-chul; Kwon, Taekyoung; Park, Juyong; Kim, Huy Kang

2012-01-01

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior. PMID:22496771

Unobtrusive Social Network Data From Email

DTIC Science & Technology

2008-12-01

PERSON Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 outlook archived files and stores that data into an SQL - database. Communication...Applications ( VBA ) program was installed on the personal computers (PC) of all participants, in the session window of their Microsoft Outlook. Details of

Test and Evaluation of Architecture-Aware Compiler Environment

DTIC Science & Technology

2011-11-01

biology, medicine, social sciences , and security applications. Challenges include extremely large graphs (the Facebook friend network has over...Operations with Temporal Binning ....................................................................... 32 4.12 Memory behavior and Energy per...five challenge problems empirically, exploring their scaling properties, computation and datatype needs, memory behavior , and temporal behavior

E-Mail in Academia: Expectations, Use, and Instructional Impact

ERIC Educational Resources Information Center

Weiss, Meredith; Hanson-Baldauf, Dana

2008-01-01

The popularity of and reliance on emergent computer-mediated communication technologies such as instant messaging, blogs, and social networks have arguably widened the generation gap between faculty and traditional undergraduate students. Marc Prensky defined this generational technology divide by coining the terms "digital natives" and…

Can Social Networking Be Used to Promote Engagement in Child Maltreatment Prevention Programs? Two Pilot Studies

PubMed Central

Edwards-Gaura, Anna; Whitaker, Daniel; Self-Brown, Shannon

2014-01-01

Introduction: Child maltreatment is one of the United States' most significant public health problems. In efforts to prevent maltreatment experts recommend use of Behavioral Parent Training Programs (BPTs), which focus on teaching skills that will replace and prevent maltreating behavior. While there is research to support the effectiveness of BPTs in maltreatment prevention, the reach of such programs is still limited by several barriers, including poor retention of families in services. Recently, new technologies have emerged that offer innovative opportunities to improve family engagement. These technologies include smartphones and social networking; however, very little is known about the potential of these to aid in maltreatment prevention. The primary goal of this study was to conduct 2 pilot exploratory projects. Methods: The first project administered a survey to parents and providers to gather data about at-risk parents' use of smartphones and online social networking technologies. The second project tested a social networking-enhanced brief parenting program with 3 intervention participants and evaluated parental responses. Results: Seventy-five percent of parents surveyed reported owning a computer that worked. Eighty-nine percent of parents reported that they had reliable Internet access at home, and 67% said they used the Internet daily. Three parents participated in the intervention with all reporting improvement in parent-child interaction skills and a positive experience participating in the social networking-enhanced SafeCare components. Conclusion: In general, findings suggest that smartphones, social networking, and Facebook, in particular, are now being used by individuals who show risk factors for maltreatment. Further, the majority of parents surveyed in this study said that they like Facebook, and all parents surveyed said that they use Facebook and have a Facebook account. As well, all saw it as a potentially beneficial supplement for future parents enrolling in parenting programs. PMID:25157304

Hierarchical sequencing of online social graphs

NASA Astrophysics Data System (ADS)

Andjelković, Miroslav; Tadić, Bosiljka; Maletić, Slobodan; Rajković, Milan

2015-10-01

In online communications, patterns of conduct of individual actors and use of emotions in the process can lead to a complex social graph exhibiting multilayered structure and mesoscopic communities. Using simplicial complexes representation of graphs, we investigate in-depth topology of the online social network constructed from MySpace dialogs which exhibits original community structure. A simulation of emotion spreading in this network leads to the identification of two emotion-propagating layers. Three topological measures are introduced, referred to as the structure vectors, which quantify graph's architecture at different dimension levels. Notably, structures emerging through shared links, triangles and tetrahedral faces, frequently occur and range from tree-like to maximal 5-cliques and their respective complexes. On the other hand, the structures which spread only negative or only positive emotion messages appear to have much simpler topology consisting of links and triangles. The node's structure vector represents the number of simplices at each topology level in which the node resides and the total number of such simplices determines what we define as the node's topological dimension. The presented results suggest that the node's topological dimension provides a suitable measure of the social capital which measures the actor's ability to act as a broker in compact communities, the so called Simmelian brokerage. We also generalize the results to a wider class of computer-generated networks. Investigating components of the node's vector over network layers reveals that same nodes develop different socio-emotional relations and that the influential nodes build social capital by combining their connections in different layers.

Network portal: a database for storage, analysis and visualization of biological networks

PubMed Central

Turkarslan, Serdar; Wurtmann, Elisabeth J.; Wu, Wei-Ju; Jiang, Ning; Bare, J. Christopher; Foley, Karen; Reiss, David J.; Novichkov, Pavel; Baliga, Nitin S.

2014-01-01

The ease of generating high-throughput data has enabled investigations into organismal complexity at the systems level through the inference of networks of interactions among the various cellular components (genes, RNAs, proteins and metabolites). The wider scientific community, however, currently has limited access to tools for network inference, visualization and analysis because these tasks often require advanced computational knowledge and expensive computing resources. We have designed the network portal (http://networks.systemsbiology.net) to serve as a modular database for the integration of user uploaded and public data, with inference algorithms and tools for the storage, visualization and analysis of biological networks. The portal is fully integrated into the Gaggle framework to seamlessly exchange data with desktop and web applications and to allow the user to create, save and modify workspaces, and it includes social networking capabilities for collaborative projects. While the current release of the database contains networks for 13 prokaryotic organisms from diverse phylogenetic clades (4678 co-regulated gene modules, 3466 regulators and 9291 cis-regulatory motifs), it will be rapidly populated with prokaryotic and eukaryotic organisms as relevant data become available in public repositories and through user input. The modular architecture, simple data formats and open API support community development of the portal. PMID:24271392

Computer viruses

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1988-01-01

The worm, Trojan horse, bacterium, and virus are destructive programs that attack information stored in a computer's memory. Virus programs, which propagate by incorporating copies of themselves into other programs, are a growing menace in the late-1980s world of unprotected, networked workstations and personal computers. Limited immunity is offered by memory protection hardware, digitally authenticated object programs,and antibody programs that kill specific viruses. Additional immunity can be gained from the practice of digital hygiene, primarily the refusal to use software from untrusted sources. Full immunity requires attention in a social dimension, the accountability of programmers.

Social Networking Adapted for Distributed Scientific Collaboration

NASA Technical Reports Server (NTRS)

Karimabadi, Homa

2012-01-01

Share is a social networking site with novel, specially designed feature sets to enable simultaneous remote collaboration and sharing of large data sets among scientists. The site will include not only the standard features found on popular consumer-oriented social networking sites such as Facebook and Myspace, but also a number of powerful tools to extend its functionality to a science collaboration site. A Virtual Observatory is a promising technology for making data accessible from various missions and instruments through a Web browser. Sci-Share augments services provided by Virtual Observatories by enabling distributed collaboration and sharing of downloaded and/or processed data among scientists. This will, in turn, increase science returns from NASA missions. Sci-Share also enables better utilization of NASA s high-performance computing resources by providing an easy and central mechanism to access and share large files on users space or those saved on mass storage. The most common means of remote scientific collaboration today remains the trio of e-mail for electronic communication, FTP for file sharing, and personalized Web sites for dissemination of papers and research results. Each of these tools has well-known limitations. Sci-Share transforms the social networking paradigm into a scientific collaboration environment by offering powerful tools for cooperative discourse and digital content sharing. Sci-Share differentiates itself by serving as an online repository for users digital content with the following unique features: a) Sharing of any file type, any size, from anywhere; b) Creation of projects and groups for controlled sharing; c) Module for sharing files on HPC (High Performance Computing) sites; d) Universal accessibility of staged files as embedded links on other sites (e.g. Facebook) and tools (e.g. e-mail); e) Drag-and-drop transfer of large files, replacing awkward e-mail attachments (and file size limitations); f) Enterprise-level data and messaging encryption; and g) Easy-to-use intuitive workflow.

Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

PubMed Central

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms. PMID:24723806

Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks.

PubMed

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media

PubMed Central

Bail, Christopher Andrew

2016-01-01

Social media sites are rapidly becoming one of the most important forums for public deliberation about advocacy issues. However, social scientists have not explained why some advocacy organizations produce social media messages that inspire far-ranging conversation among social media users, whereas the vast majority of them receive little or no attention. I argue that advocacy organizations are more likely to inspire comments from new social media audiences if they create “cultural bridges,” or produce messages that combine conversational themes within an advocacy field that are seldom discussed together. I use natural language processing, network analysis, and a social media application to analyze how cultural bridges shaped public discourse about autism spectrum disorders on Facebook over the course of 1.5 years, controlling for various characteristics of advocacy organizations, their social media audiences, and the broader social context in which they interact. I show that organizations that create substantial cultural bridges provoke 2.52 times more comments about their messages from new social media users than those that do not, controlling for these factors. This study thus offers a theory of cultural messaging and public deliberation and computational techniques for text analysis and application-based survey research. PMID:27694580

What do computer scientists tweet? Analyzing the link-sharing practice on Twitter.

PubMed

Schmitt, Marco; Jäschke, Robert

2017-01-01

Twitter communication has permeated every sphere of society. To highlight and share small pieces of information with possibly vast audiences or small circles of the interested has some value in almost any aspect of social life. But what is the value exactly for a scientific field? We perform a comprehensive study of computer scientists using Twitter and their tweeting behavior concerning the sharing of web links. Discerning the domains, hosts and individual web pages being tweeted and the differences between computer scientists and a Twitter sample enables us to look in depth at the Twitter-based information sharing practices of a scientific community. Additionally, we aim at providing a deeper understanding of the role and impact of altmetrics in computer science and give a glance at the publications mentioned on Twitter that are most relevant for the computer science community. Our results show a link sharing culture that concentrates more heavily on public and professional quality information than the Twitter sample does. The results also show a broad variety in linked sources and especially in linked publications with some publications clearly related to community-specific interests of computer scientists, while others with a strong relation to attention mechanisms in social media. This refers to the observation that Twitter is a hybrid form of social media between an information service and a social network service. Overall the computer scientists' style of usage seems to be more on the information-oriented side and to some degree also on professional usage. Therefore, altmetrics are of considerable use in analyzing computer science.

What do computer scientists tweet? Analyzing the link-sharing practice on Twitter

PubMed Central

Schmitt, Marco

2017-01-01

Twitter communication has permeated every sphere of society. To highlight and share small pieces of information with possibly vast audiences or small circles of the interested has some value in almost any aspect of social life. But what is the value exactly for a scientific field? We perform a comprehensive study of computer scientists using Twitter and their tweeting behavior concerning the sharing of web links. Discerning the domains, hosts and individual web pages being tweeted and the differences between computer scientists and a Twitter sample enables us to look in depth at the Twitter-based information sharing practices of a scientific community. Additionally, we aim at providing a deeper understanding of the role and impact of altmetrics in computer science and give a glance at the publications mentioned on Twitter that are most relevant for the computer science community. Our results show a link sharing culture that concentrates more heavily on public and professional quality information than the Twitter sample does. The results also show a broad variety in linked sources and especially in linked publications with some publications clearly related to community-specific interests of computer scientists, while others with a strong relation to attention mechanisms in social media. This refers to the observation that Twitter is a hybrid form of social media between an information service and a social network service. Overall the computer scientists’ style of usage seems to be more on the information-oriented side and to some degree also on professional usage. Therefore, altmetrics are of considerable use in analyzing computer science. PMID:28636619

A survey of body sensor networks.

PubMed

Lai, Xiaochen; Liu, Quanli; Wei, Xin; Wang, Wei; Zhou, Guoqiao; Han, Guangyi

2013-04-24

The technology of sensor, pervasive computing, and intelligent information processing is widely used in Body Sensor Networks (BSNs), which are a branch of wireless sensor networks (WSNs). BSNs are playing an increasingly important role in the fields of medical treatment, social welfare and sports, and are changing the way humans use computers. Existing surveys have placed emphasis on the concept and architecture of BSNs, signal acquisition, context-aware sensing, and system technology, while this paper will focus on sensor, data fusion, and network communication. And we will introduce the research status of BSNs, the analysis of hotspots, and future development trends, the discussion of major challenges and technical problems facing currently. The typical research projects and practical application of BSNs are introduced as well. BSNs are progressing along the direction of multi-technology integration and intelligence. Although there are still many problems, the future of BSNs is fundamentally promising, profoundly changing the human-machine relationships and improving the quality of people's lives.

A Survey of Body Sensor Networks

PubMed Central

Lai, Xiaochen; Liu, Quanli; Wei, Xin; Wang, Wei; Zhou, Guoqiao; Han, Guangyi

2013-01-01

The technology of sensor, pervasive computing, and intelligent information processing is widely used in Body Sensor Networks (BSNs), which are a branch of wireless sensor networks (WSNs). BSNs are playing an increasingly important role in the fields of medical treatment, social welfare and sports, and are changing the way humans use computers. Existing surveys have placed emphasis on the concept and architecture of BSNs, signal acquisition, context-aware sensing, and system technology, while this paper will focus on sensor, data fusion, and network communication. And we will introduce the research status of BSNs, the analysis of hotspots, and future development trends, the discussion of major challenges and technical problems facing currently. The typical research projects and practical application of BSNs are introduced as well. BSNs are progressing along the direction of multi-technology integration and intelligence. Although there are still many problems, the future of BSNs is fundamentally promising, profoundly changing the human-machine relationships and improving the quality of people's lives. PMID:23615581

Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme

PubMed Central

Xu, Yang; Luo, Xiong; Wang, Weiping; Zhao, Wenbing

2017-01-01

Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor nodes has become an essential requirement for many applications over WSN. Meanwhile, the localization information of unknown nodes has strongly affected the performance of WSN. The received signal strength indication (RSSI) as a typical range-based algorithm for positioning sensor nodes in WSN could achieve accurate location with hardware saving, but is sensitive to environmental noises. Moreover, the original distance vector hop (DV-HOP) as an important range-free localization algorithm is simple, inexpensive and not related to the environment factors, but performs poorly when lacking anchor nodes. Motivated by these, various improved DV-HOP schemes with RSSI have been introduced, and we present a new neural network (NN)-based node localization scheme, named RHOP-ELM-RCC, through the use of DV-HOP, RSSI and a regularized correntropy criterion (RCC)-based extreme learning machine (ELM) algorithm (ELM-RCC). Firstly, the proposed scheme employs both RSSI and DV-HOP to evaluate the distances between nodes to enhance the accuracy of distance estimation at a reasonable cost. Then, with the help of ELM featured with a fast learning speed with a good generalization performance and minimal human intervention, a single hidden layer feedforward network (SLFN) on the basis of ELM-RCC is used to implement the optimization task for obtaining the location of unknown nodes. Since the RSSI may be influenced by the environmental noises and may bring estimation error, the RCC instead of the mean square error (MSE) estimation, which is sensitive to noises, is exploited in ELM. Hence, it may make the estimation more robust against outliers. Additionally, the least square estimation (LSE) in ELM is replaced by the half-quadratic optimization technique. Simulation results show that our proposed scheme outperforms other traditional localization schemes. PMID:28085084

A study of the spreading scheme for viral marketing based on a complex network model

NASA Astrophysics Data System (ADS)

Yang, Jianmei; Yao, Canzhong; Ma, Weicheng; Chen, Guanrong

2010-02-01

Buzzword-based viral marketing, known also as digital word-of-mouth marketing, is a marketing mode attached to some carriers on the Internet, which can rapidly copy marketing information at a low cost. Viral marketing actually uses a pre-existing social network where, however, the scale of the pre-existing network is believed to be so large and so random, so that its theoretical analysis is intractable and unmanageable. There are very few reports in the literature on how to design a spreading scheme for viral marketing on real social networks according to the traditional marketing theory or the relatively new network marketing theory. Complex network theory provides a new model for the study of large-scale complex systems, using the latest developments of graph theory and computing techniques. From this perspective, the present paper extends the complex network theory and modeling into the research of general viral marketing and develops a specific spreading scheme for viral marking and an approach to design the scheme based on a real complex network on the QQ instant messaging system. This approach is shown to be rather universal and can be further extended to the design of various spreading schemes for viral marketing based on different instant messaging systems.

User friendly IT Services for Monitoring and Prevention during Pregnancy.

PubMed

Crişan-Vida, Mihaela; Serban, Alexandru; Ghihor-Izdrăilă, Ioana; Mirea, Adrian; Stoicu-Tivadar, Lacramioara

2014-01-01

A healthy lifestyle for a mother and monitoring both mother and fetus activities are crucial factors for a normal pregnancy without hazardous conditions. This paper proposes a cloud computing solution and a mobile application which collect data from the sensors to be used in Obstetrics-Gynecology Department. This application monitors the dietary plan of the pregnant and gives her the possibility to socialize and share pregnancy experience with the rest of women from the social network from the hospital. The physicians can access the information's of the patient in real time and they can alert mothers in some situations. Using this cloud computing device, the health condition of the pregnant women may be improved.

A decision network account of reasoning about other people's choices

PubMed Central

Jern, Alan; Kemp, Charles

2015-01-01

The ability to predict and reason about other people's choices is fundamental to social interaction. We propose that people reason about other people's choices using mental models that are similar to decision networks. Decision networks are extensions of Bayesian networks that incorporate the idea that choices are made in order to achieve goals. In our first experiment, we explore how people predict the choices of others. Our remaining three experiments explore how people infer the goals and knowledge of others by observing the choices that they make. We show that decision networks account for our data better than alternative computational accounts that do not incorporate the notion of goal-directed choice or that do not rely on probabilistic inference. PMID:26010559

The Relative Ineffectiveness of Criminal Network Disruption

PubMed Central

Duijn, Paul A. C.; Kashirin, Victor; Sloot, Peter M. A.

2014-01-01

Researchers, policymakers and law enforcement agencies across the globe struggle to find effective strategies to control criminal networks. The effectiveness of disruption strategies is known to depend on both network topology and network resilience. However, as these criminal networks operate in secrecy, data-driven knowledge concerning the effectiveness of different criminal network disruption strategies is very limited. By combining computational modeling and social network analysis with unique criminal network intelligence data from the Dutch Police, we discovered, in contrast to common belief, that criminal networks might even become ‘stronger’, after targeted attacks. On the other hand increased efficiency within criminal networks decreases its internal security, thus offering opportunities for law enforcement agencies to target these networks more deliberately. Our results emphasize the importance of criminal network interventions at an early stage, before the network gets a chance to (re-)organize to maximum resilience. In the end disruption strategies force criminal networks to become more exposed, which causes successful network disruption to become a long-term effort. PMID:24577374

The relative ineffectiveness of criminal network disruption.

PubMed

Duijn, Paul A C; Kashirin, Victor; Sloot, Peter M A

2014-02-28

Researchers, policymakers and law enforcement agencies across the globe struggle to find effective strategies to control criminal networks. The effectiveness of disruption strategies is known to depend on both network topology and network resilience. However, as these criminal networks operate in secrecy, data-driven knowledge concerning the effectiveness of different criminal network disruption strategies is very limited. By combining computational modeling and social network analysis with unique criminal network intelligence data from the Dutch Police, we discovered, in contrast to common belief, that criminal networks might even become 'stronger', after targeted attacks. On the other hand increased efficiency within criminal networks decreases its internal security, thus offering opportunities for law enforcement agencies to target these networks more deliberately. Our results emphasize the importance of criminal network interventions at an early stage, before the network gets a chance to (re-)organize to maximum resilience. In the end disruption strategies force criminal networks to become more exposed, which causes successful network disruption to become a long-term effort.

Detection of communities with Naming Game-based methods

PubMed Central

Ribeiro, Carlos Henrique Costa

2017-01-01

Complex networks are often organized in groups or communities of agents that share the same features and/or functions, and this structural organization is built naturally with the formation of the system. In social networks, we argue that the dynamic of linguistic interactions of agreement among people can be a crucial factor in generating this community structure, given that sharing opinions with another person bounds them together, and disagreeing constantly would probably weaken the relationship. We present here a computational model of opinion exchange that uncovers the community structure of a network. Our aim is not to present a new community detection method proper, but to show how a model of social communication dynamics can reveal the (simple and overlapping) community structure in an emergent way. Our model is based on a standard Naming Game, but takes into consideration three social features: trust, uncertainty and opinion preference, that are built over time as agents communicate among themselves. We show that the separate addition of each social feature in the Naming Game results in gradual improvements with respect to community detection. In addition, the resulting uncertainty and trust values classify nodes and edges according to role and position in the network. Also, our model has shown a degree of accuracy both for non-overlapping and overlapping communities that are comparable with most algorithms specifically designed for topological community detection. PMID:28797097

Workshop on Incomplete Network Data Held at Sandia National Labs – Livermore

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

Soundarajan, Sucheta; Wendt, Jeremy D.

2016-06-01

While network analysis is applied in a broad variety of scientific fields (including physics, computer science, biology, and the social sciences), how networks are constructed and the resulting bias and incompleteness have drawn more limited attention. For example, in biology, gene networks are typically developed via experiment -- many actual interactions are likely yet to be discovered. In addition to this incompleteness, the data-collection processes can introduce significant bias into the observed network datasets. For instance, if you observe part of the World Wide Web network through a classic random walk, then high degree nodes are more likely to bemore » found than if you had selected nodes at random. Unfortunately, such incomplete and biasing data collection methods must be often used.« less

Community structure in networks

NASA Astrophysics Data System (ADS)

Newman, Mark

2004-03-01

Many networked systems, including physical, biological, social, and technological networks, appear to contain ``communities'' -- groups of nodes within which connections are dense, but between which they are sparser. The ability to find such communities in an automated fashion could be of considerable use. Communities in a web graph for instance might correspond to sets of web sites dealing with related topics, while communities in a biochemical network or an electronic circuit might correspond to functional units of some kind. We present a number of new methods for community discovery, including methods based on ``betweenness'' measures and methods based on modularity optimization. We also give examples of applications of these methods to both computer-generated and real-world network data, and show how our techniques can be used to shed light on the sometimes dauntingly complex structure of networked systems.

Computational social network modeling of terrorist recruitment.

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

Berry, Nina M.; Turnley, Jessica Glicken; Smrcka, Julianne D.

2004-10-01

The Seldon terrorist model represents a multi-disciplinary approach to developing organization software for the study of terrorist recruitment and group formation. The need to incorporate aspects of social science added a significant contribution to the vision of the resulting Seldon toolkit. The unique addition of and abstract agent category provided a means for capturing social concepts like cliques, mosque, etc. in a manner that represents their social conceptualization and not simply as a physical or economical institution. This paper provides an overview of the Seldon terrorist model developed to study the formation of cliques, which are used as the majormore » recruitment entity for terrorist organizations.« less

Open Source software and social networks: disruptive alternatives for medical imaging.

PubMed

Ratib, Osman; Rosset, Antoine; Heuberger, Joris

2011-05-01

In recent decades several major changes in computer and communication technology have pushed the limits of imaging informatics and PACS beyond the traditional system architecture providing new perspectives and innovative approach to a traditionally conservative medical community. Disruptive technologies such as the world-wide-web, wireless networking, Open Source software and recent emergence of cyber communities and social networks have imposed an accelerated pace and major quantum leaps in the progress of computer and technology infrastructure applicable to medical imaging applications. This paper reviews the impact and potential benefits of two major trends in consumer market software development and how they will influence the future of medical imaging informatics. Open Source software is emerging as an attractive and cost effective alternative to traditional commercial software developments and collaborative social networks provide a new model of communication that is better suited to the needs of the medical community. Evidence shows that successful Open Source software tools have penetrated the medical market and have proven to be more robust and cost effective than their commercial counterparts. Developed by developers that are themselves part of the user community, these tools are usually better adapted to the user's need and are more robust than traditional software programs being developed and tested by a large number of contributing users. This context allows a much faster and more appropriate development and evolution of the software platforms. Similarly, communication technology has opened up to the general public in a way that has changed the social behavior and habits adding a new dimension to the way people communicate and interact with each other. The new paradigms have also slowly penetrated the professional market and ultimately the medical community. Secure social networks allowing groups of people to easily communicate and exchange information is a new model that is particularly suitable for some specific groups of healthcare professional and for physicians. It has also changed the expectations of how patients wish to communicate with their physicians. Emerging disruptive technologies and innovative paradigm such as Open Source software are leading the way to a new generation of information systems that slowly will change the way physicians and healthcare providers as well as patients will interact and communicate in the future. The impact of these new technologies is particularly effective in image communication, PACS and teleradiology. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Technology Education and Development

ERIC Educational Resources Information Center

Lazinica, Aleksandar, Ed.; Calafate, Carlos, Ed.

2009-01-01

The widespread deployment and use of Information Technologies (IT) has paved the way for change in many fields of our societies. The Internet, mobile computing, social networks and many other advances in human communications have become essential to promote and boost education, technology and industry. On the education side, the new challenges…

Remote Labs and Game-Based Learning for Process Control

ERIC Educational Resources Information Center

Zualkernan, Imran A.; Husseini, Ghaleb A.; Loughlin, Kevin F.; Mohebzada, Jamshaid G.; El Gaml, Moataz

2013-01-01

Social networking platforms and computer games represent a natural informal learning environment for the current generation of learners in higher education. This paper explores the use of game-based learning in the context of an undergraduate chemical engineering remote laboratory. Specifically, students are allowed to manipulate chemical…

Tough New Issues Refocus Health Education

ERIC Educational Resources Information Center

Dessoff, Alan

2011-01-01

From dating violence to sexting and social networking, districts are struggling to address a number of sensitive and relatively new health education issues that are aggravated by students' increasing access to computers, cell phones and other digital devices. Through new or revised curricula, administrators are attempting to deal with these and…

Students' Orientation towards Interpersonal Communication in Online Social Networking Sites

ERIC Educational Resources Information Center

Kayode, Bakare Kazeem; Zamzami, Ikhlas F.; Olowolayemo, Akeem

2012-01-01

Purpose: As computer-mediated communication has diffused, successive technological variations raise new questions about interpersonal impressions and several standardized instruments have been advanced in literature to asses various aspect of interpersonal attraction phenomena. The purpose of this paper is to examine the claims for reliability and…

Designing Effective Persuasive Systems Utilizing the Power of Entanglement: Communication Channel, Strategy & Affect

ERIC Educational Resources Information Center

Li, Haiqing

2010-01-01

With rapid advancements in information and communication technologies, computer-mediated communication channels such as email, web, mobile smart-phones with SMS, social networking websites (Facebook), multimedia websites, and OEM devices provide users with multiple technology choices to seek information. However, no study has compared the…

Photograph use on social network sites among South Korean college students: the role of public and private self-consciousness.

PubMed

Shim, Minsun; Lee, Min Ju; Park, Sang Hee

2008-08-01

This study challenges theories of computer-mediated communication (CMC) that typically assume a lack of visual cues in CMC and conceptualize them as a situational factor. We examine individual differences in photo use on social network sites and link them to the dispositional tendency of self-consciousness (SC). Included were 231 students from one university in South Korea, who owned and maintained Cyworld profiles (called "mini-homepages"). Findings suggest that public SC, but not private SC, was positively associated with the higher frequency of posting photos, replying to comments on the photos, and scrapping photos on their mini-homepages. The frequency of posting specific content types of photos (concerning social relationships or life events) was also determined by participants' public SC but not by private SC. Implications of the findings for CMC theories are discussed.

Discovering Network Structure Beyond Communities

NASA Astrophysics Data System (ADS)

Nishikawa, Takashi; Motter, Adilson E.

2011-11-01

To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.

Social Network Modeling and Simulation of Integrated Resilient Command and Control (C2) in Contested Cyber Environments

DTIC Science & Technology

2011-12-09

traced to non-state actors it provided the impetus to the creation of Joint Task Force Computer Network Defense (JTF-CND). Since the creation of JTF...telecommunications and IT systems. One of those many efforts by the USAF has been the creation of the 24th Air Force (24th AF), also known as US Air Force...Support For Organizational Structures, Policies, Technologies and People to Improve Resilience Prior to creation of USCYBERCOM, responsibility for

Global brain dynamics during social exclusion predict subsequent behavioral conformity

PubMed Central

Wasylyshyn, Nick; Hemenway Falk, Brett; Garcia, Javier O; Cascio, Christopher N; O’Donnell, Matthew Brook; Bingham, C Raymond; Simons-Morton, Bruce; Vettel, Jean M; Falk, Emily B

2018-01-01

Abstract Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences. PMID:29529310

Distributed micro-releases of bioterror pathogens : threat characterizations and epidemiology from uncertain patient observables.

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

Wolf, Michael M.; Marzouk, Youssef M.; Adams, Brian M.

2008-10-01

Terrorist attacks using an aerosolized pathogen preparation have gained credibility as a national security concern since the anthrax attacks of 2001. The ability to characterize the parameters of such attacks, i.e., to estimate the number of people infected, the time of infection, the average dose received, and the rate of disease spread in contemporary American society (for contagious diseases), is important when planning a medical response. For non-contagious diseases, we address the characterization problem by formulating a Bayesian inverse problem predicated on a short time-series of diagnosed patients exhibiting symptoms. To keep the approach relevant for response planning, we limitmore » ourselves to 3.5 days of data. In computational tests performed for anthrax, we usually find these observation windows sufficient, especially if the outbreak model employed in the inverse problem is accurate. For contagious diseases, we formulated a Bayesian inversion technique to infer both pathogenic transmissibility and the social network from outbreak observations, ensuring that the two determinants of spreading are identified separately. We tested this technique on data collected from a 1967 smallpox epidemic in Abakaliki, Nigeria. We inferred, probabilistically, different transmissibilities in the structured Abakaliki population, the social network, and the chain of transmission. Finally, we developed an individual-based epidemic model to realistically simulate the spread of a rare (or eradicated) disease in a modern society. This model incorporates the mixing patterns observed in an (American) urban setting and accepts, as model input, pathogenic transmissibilities estimated from historical outbreaks that may have occurred in socio-economic environments with little resemblance to contemporary society. Techniques were also developed to simulate disease spread on static and sampled network reductions of the dynamic social networks originally in the individual-based model, yielding faster, though approximate, network-based epidemic models. These reduced-order models are useful in scenario analysis for medical response planning, as well as in computationally intensive inverse problems.« less

Integration of individual and social information for decision-making in groups of different sizes

PubMed Central

Goïame, Sidney; O'Connor, David A.; Dreher, Jean-Claude

2017-01-01

When making judgments in a group, individuals often revise their initial beliefs about the best judgment to make given what others believe. Despite the ubiquity of this phenomenon, we know little about how the brain updates beliefs when integrating personal judgments (individual information) with those of others (social information). Here, we investigated the neurocomputational mechanisms of how we adapt our judgments to those made by groups of different sizes, in the context of jury decisions for a criminal. By testing different theoretical models, we showed that a social Bayesian inference model captured changes in judgments better than 2 other models. Our results showed that participants updated their beliefs by appropriately weighting individual and social sources of information according to their respective credibility. When investigating 2 fundamental computations of Bayesian inference, belief updates and credibility estimates of social information, we found that the dorsal anterior cingulate cortex (dACC) computed the level of belief updates, while the bilateral frontopolar cortex (FPC) was more engaged in individuals who assigned a greater credibility to the judgments of a larger group. Moreover, increased functional connectivity between these 2 brain regions reflected a greater influence of group size on the relative credibility of social information. These results provide a mechanistic understanding of the computational roles of the FPC-dACC network in steering judgment adaptation to a group’s opinion. Taken together, these findings provide a computational account of how the human brain integrates individual and social information for decision-making in groups. PMID:28658252

Finding the probability of infection in an SIR network is NP-Hard

PubMed Central

Shapiro, Michael; Delgado-Eckert, Edgar

2012-01-01

It is the purpose of this article to review results that have long been known to communications network engineers and have direct application to epidemiology on networks. A common approach in epidemiology is to study the transmission of a disease in a population where each individual is initially susceptible (S), may become infective (I) and then removed or recovered (R) and plays no further epidemiological role. Much of the recent work gives explicit consideration to the network of social interactions or disease-transmitting contacts and attendant probability of transmission for each interacting pair. The state of such a network is an assignment of the values {S, I, R} to its members. Given such a network, an initial state and a particular susceptible individual, we would like to compute their probability of becoming infected in the course of an epidemic. It turns out that this and related problems are NP-hard. In particular, it belongs in a class of problems for which no efficient algorithms for their solution are known. Moreover, finding an efficient algorithm for the solution of any problem in this class would entail a major breakthrough in theoretical computer science. PMID:22824138

Research on the information security system in electrical gis system in mobile application

NASA Astrophysics Data System (ADS)

Zhou, Chao; Feng, Renjun; Jiang, Haitao; Huang, Wei; Zhu, Daohua

2017-05-01

With the rapid development of social informatization process, the demands of government, enterprise, and individuals for spatial information becomes larger. In addition, the combination of wireless network technology and spatial information technology promotes the generation and development of mobile technologies. In today’s rapidly developed information technology field, network technology and mobile communication have become the two pillar industries by leaps and bounds. They almost absorbed and adopted all the latest information, communication, computer, electronics and so on new technologies. Concomitantly, the network coverage is more and more big, the transmission rate is faster and faster, the volume of user’s terminal is smaller and smaller. What’s more, from LAN to WAN, from wired network to wireless network, from wired access to mobile wireless access, people’s demand for communication technology is increasingly higher. As a result, mobile communication technology is facing unprecedented challenges as well as unprecedented opportunities. When combined with the existing mobile communication network, it led to the development of leaps and bounds. However, due to the inherent dependence of the system on the existing computer communication network, information security problems cannot be ignored. Today’s information security has penetrated into all aspects of life. Information system is a complex computer system, and it’s physical, operational and management vulnerabilities constitute the security vulnerability of the system. Firstly, this paper analyzes the composition of mobile enterprise network and information security threat. Secondly, this paper puts forward the security planning and measures, and constructs the information security structure.

Globalisation and human dignity: the case of the information superhighway.

PubMed

Hamelink, C J

1996-01-01

In 1994, Vice President Al Gore coined the concept of the Information Superhighway during a speech in Buenos Aires in which he proposed the development of a global information infrastructure. The project envisions the incorporation of all existing communication networks into one system, facilitating the globalization of markets. The internet, a decentralized network of computer networks, is one small-scale, existing model of what the superhighway could be, a public space owned by nobody in which communication takes place largely for noncommercial purposes. The internet currently connects 40 million computer users from at least 90 countries. The alternative model for the superhighway is the privately managed global shopping mall, a global interactive marketplace for information, entertainment, and advertising. The author warns, however, that it is not possible to predict the future social impact of such an information superhighway. Decision makers nonetheless push ahead in development, full of confidence in the merit and infallibility of technology. Since one cannot accurately predict the future, it rational to assume that social consequences may both promote and threaten human dignity. The author explains at which level he believes the superhighway threatens the human rights norms of equality, inviolability, and liberty, and discusses what the World Association for Christian Communication can do.

Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks

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

Visweswara Sathanur, Arun; Halappanavar, Mahantesh; Shi, Yi

In many complex networked systems such as online social networks, at any given time, activity originates at certain nodes and subsequently spreads on the network through influence. To model the spread of influence in such a scenario, we consider the problem of identification of influential entities in a complex network when nodal activation can happen through two different mechanisms. The first mode of activation is due mechanisms intrinsic to the node. The second mechanism is through the influence of connected neighbors. In this work, we present a simple probabilistic formulation that models such self-evolving systems where information diffusion occurs primarilymore » because of the intrinsic activity of users and the spread of activity occurs due to influence. We provide an algorithm to mine for the influential seeds in such a scenario by modifying the well-known influence maximization framework with the independent cascade diffusion model. We provide small motivating examples to provide an intuitive understanding of the effect of including the intrinsic activation mechanism. We sketch a proof of the submodularity of the influence function under the new formulation and demonstrate the same with larger graphs. We then show by means of additional experiments on a real-world twitter dataset how the formulation can be applied to real-world social media datasets. Finally we derive a computationally efficient centrality metric that takes into account, both the mechanisms of activation and provides for an accurate as well as computationally efficient alternative approach to the problem of identifying influencers under intrinsic activation.« less

Computing Systemic Risk Using Multiple Behavioral and Keystone Networks: The Emergence of a Crisis in Primate Societies and Banks*

PubMed Central

Fushing, Hsieh; Jordà, Òscar; Beisner, Brianne; McCowan, Brenda

2015-01-01

What do the behavior of monkeys in captivity and the financial system have in common? The nodes in such social systems relate to each other through multiple and keystone networks, not just one network. Each network in the system has its own topology, and the interactions among the system’s networks change over time. In such systems, the lead into a crisis appears to be characterized by a decoupling of the networks from the keystone network. This decoupling can also be seen in the crumbling of the keystone’s power structure toward a more horizontal hierarchy. This paper develops nonparametric methods for describing the joint model of the latent architecture of interconnected networks in order to describe this process of decoupling, and hence provide an early warning system of an impending crisis. PMID:26056422

Nature as a network of morphological infocomputational processes for cognitive agents

NASA Astrophysics Data System (ADS)

Dodig-Crnkovic, Gordana

2017-01-01

This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological computational models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted.

The scientific research potential of virtual worlds.

PubMed

Bainbridge, William Sims

2007-07-27

Online virtual worlds, electronic environments where people can work and interact in a somewhat realistic manner, have great potential as sites for research in the social, behavioral, and economic sciences, as well as in human-centered computer science. This article uses Second Life and World of Warcraft as two very different examples of current virtual worlds that foreshadow future developments, introducing a number of research methodologies that scientists are now exploring, including formal experimentation, observational ethnography, and quantitative analysis of economic markets or social networks.

A Family of Algorithms for Computing Consensus about Node State from Network Data

PubMed Central

Brush, Eleanor R.; Krakauer, David C.; Flack, Jessica C.

2013-01-01

Biological and social networks are composed of heterogeneous nodes that contribute differentially to network structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual nodes–from ranking websites to determining critical species in ecosystems–yet the mechanistic basis for why they produce good rankings remains poorly understood. We show that a unifying property of these algorithms is that they quantify consensus in the network about a node's state or capacity to perform a function. The algorithms capture consensus by either taking into account the number of a target node's direct connections, and, when the edges are weighted, the uniformity of its weighted in-degree distribution (breadth), or by measuring net flow into a target node (depth). Using data from communication, social, and biological networks we find that that how an algorithm measures consensus–through breadth or depth– impacts its ability to correctly score nodes. We also observe variation in sensitivity to source biases in interaction/adjacency matrices: errors arising from systematic error at the node level or direct manipulation of network connectivity by nodes. Our results indicate that the breadth algorithms, which are derived from information theory, correctly score nodes (assessed using independent data) and are robust to errors. However, in cases where nodes “form opinions” about other nodes using indirect information, like reputation, depth algorithms, like Eigenvector Centrality, are required. One caveat is that Eigenvector Centrality is not robust to error unless the network is transitive or assortative. In these cases the network structure allows the depth algorithms to effectively capture breadth as well as depth. Finally, we discuss the algorithms' cognitive and computational demands. This is an important consideration in systems in which individuals use the collective opinions of others to make decisions. PMID:23874167

Social computing for image matching

PubMed Central

Rivas, Alberto; Sánchez-Torres, Ramiro; Rodríguez, Sara

2018-01-01

One of the main technological trends in the last five years is mass data analysis. This trend is due in part to the emergence of concepts such as social networks, which generate a large volume of data that can provide added value through their analysis. This article is focused on a business and employment-oriented social network. More specifically, it focuses on the analysis of information provided by different users in image form. The images are analyzed to detect whether other existing users have posted or talked about the same image, even if the image has undergone some type of modification such as watermarks or color filters. This makes it possible to establish new connections among unknown users by detecting what they are posting or whether they are talking about the same images. The proposed solution consists of an image matching algorithm, which is based on the rapid calculation and comparison of hashes. However, there is a computationally expensive aspect in charge of revoking possible image transformations. As a result, the image matching process is supported by a distributed forecasting system that enables or disables nodes to serve all the possible requests. The proposed system has shown promising results for matching modified images, especially when compared with other existing systems. PMID:29813082

Why we interact: on the functional role of the striatum in the subjective experience of social interaction.

PubMed

Pfeiffer, Ulrich J; Schilbach, Leonhard; Timmermans, Bert; Kuzmanovic, Bojana; Georgescu, Alexandra L; Bente, Gary; Vogeley, Kai

2014-11-01

There is ample evidence that human primates strive for social contact and experience interactions with conspecifics as intrinsically rewarding. Focusing on gaze behavior as a crucial means of human interaction, this study employed a unique combination of neuroimaging, eye-tracking, and computer-animated virtual agents to assess the neural mechanisms underlying this component of behavior. In the interaction task, participants believed that during each interaction the agent's gaze behavior could either be controlled by another participant or by a computer program. Their task was to indicate whether they experienced a given interaction as an interaction with another human participant or the computer program based on the agent's reaction. Unbeknownst to them, the agent was always controlled by a computer to enable a systematic manipulation of gaze reactions by varying the degree to which the agent engaged in joint attention. This allowed creating a tool to distinguish neural activity underlying the subjective experience of being engaged in social and non-social interaction. In contrast to previous research, this allows measuring neural activity while participants experience active engagement in real-time social interactions. Results demonstrate that gaze-based interactions with a perceived human partner are associated with activity in the ventral striatum, a core component of reward-related neurocircuitry. In contrast, interactions with a computer-driven agent activate attention networks. Comparisons of neural activity during interaction with behaviorally naïve and explicitly cooperative partners demonstrate different temporal dynamics of the reward system and indicate that the mere experience of engagement in social interaction is sufficient to recruit this system. Copyright © 2014 Elsevier Inc. All rights reserved.

Cyberbullying: a 21st Century Health Care Phenomenon.

PubMed

Carter, Jemica M; Wilson, Feleta L

2015-01-01

This study examined bullying and cyberbullying prevalence among 367 adolescents 10 to 18 years of age who were attending schools and community organizations in suburban and urban neighborhoods in the Midwest United States. The correlational design investigated adolescents' daily use of technology that could be used to cyberbully peers, such as cell phones, computers, email, and the Internet. Results showed that 30% of participants had been bullied during school, and 17% had been cyberbullied, with online social networking sites the most common media employed (68%). The majority of participants owned or had access to computers (92%), email accounts (88%), social networking accounts (e.g., Facebook or MySpace) (82%), and cell phones (79%). Daily technology use included an average of two hours on a computer and a median of 71 text messages per day. Logistic regression analysis revealed no significant differences in bullying or cyberbullying prevalence based on location (urban or suburban) or demographic characteristics. Given the substantial presence of cyberbullying and the increase in technology use among adolescents in the 21st century, nurses need knowledge of the phenomenon to plan assessments in clinical practice. Early identification and assessment of cyberbullying victims and perpetrators, and development and implementation of effective interventions are needed to reduce this form of bullying among adolescents.

No Place to Play: Current Employee Privacy Rights in Social Networking Sites

ERIC Educational Resources Information Center

Genova, Gina L.

2009-01-01

Employers have legitimate business interests in monitoring workplace Internet use: to minimize legal exposure, to increase productivity, and to avoid proprietary information loss. Since employees arguably have no expectation of privacy in their work on employers' computers, there are few grounds for complaint if they are disciplined for straying…

A Conceptual Framework Based on Activity Theory for Mobile CSCL

ERIC Educational Resources Information Center

Zurita, Gustavo; Nussbaum, Miguel

2007-01-01

There is a need for collaborative group activities that promote student social interaction in the classroom. Handheld computers interconnected by a wireless network allow people who work on a common task to interact face to face while maintaining the mediation afforded by a technology-based system. Wirelessly interconnected handhelds open up new…

Pervasive Knowledge, Social Networks, and Cloud Computing: E-Learning 2.0

ERIC Educational Resources Information Center

Anshari, Muhammad; Alas, Yabit; Guan, Lim Sei

2015-01-01

Embedding Web 2.0 in learning processes has extended learning from traditional based learning-centred to a collaborative based learning-centred institution that emphasises learning anywhere and anytime. While deploying Semantic Web into e-learning offers a broader spectrum of pervasive knowledge acquisition to enrich users' experience in learning.…

Impact of Collaborative Project-Based Learning on Self-Efficacy of Urban Minority Students in Engineering

ERIC Educational Resources Information Center

Chen, Pearl; Hernandez, Anthony; Dong, Jane

2015-01-01

This paper presents an interdisciplinary research project that studies the impact of collaborative project-based learning (CPBL) on the development of self-efficacy of students from various ethnic groups in an undergraduate senior-level computer networking class. Grounded in social constructivist and situated theories of learning, the study…

"iSpy": Seeing What Students Really Do Online

ERIC Educational Resources Information Center

Junco, Reynol

2014-01-01

Numerous studies have shown that college students use computers, the internet, and social networking websites (SNS) at high rates; however, all of these studies have relied on self-report measures of technology use. Research in other areas of human behavior has shown that self-report measures are considerably inaccurate when compared to actual…

Reality Is Our Laboratory: Communities of Practice in Applied Computer Science

ERIC Educational Resources Information Center

Rohde, M.; Klamma, R.; Jarke, M.; Wulf, V.

2007-01-01

The present paper presents a longitudinal study of the course "High-tech Entrepreneurship and New Media." The course design is based on socio-cultural theories of learning and considers the role of social capital in entrepreneurial networks. By integrating student teams into the communities of practice of local start-ups, we offer…

Past, Present, and Future Trends in Teaching Clinical Skills through Web-Based Learning Environments

ERIC Educational Resources Information Center

Coe Regan, Jo Ann R.; Youn, Eric J.

2008-01-01

Distance education in social work has grown significantly due to the use of interactive television and computer networks. Given the recent developments in delivering distance education utilizing Web-based technology, this article presents a literature review focused on identifying generational trends in the development of Web-based learning…

Novel Techniques for Secure Use of Public Cloud Computing Resources

DTIC Science & Technology

2015-09-17

applications such as Twitter and Instagram . It allows users to post messages to their public feed, where any followers of that feed will receive them...86]) and topic based systems, this work focuses on a topic based systems. Due to the recent popularity of social networks such as Twitter and Instagram

Games and Web 2.0: A Winning Combination for Millennials

ERIC Educational Resources Information Center

Spiegelman, Marsha; Glass, Richard

2009-01-01

Gaming and social networking define the millennial student. This research focuses on an evolving collaboration between 2 faculty members of different disciplines who merged Web 2.0 and game scenarios to infuse research techniques as integral components of math/computer science courses. Blogs and wikis facilitated student-faculty interaction beyond…

Effect of Zealotry in High-dimensional Opinion Dynamics Models

DTIC Science & Technology

2015-02-18

the several platforms for portable computing to choose from, such as Apple iPad, Amazon Kindle, and Samsung Galaxy Note. A third example is the choice... diverse topologies, Chaos 17, 026111 (2007). [16] S. K. Maity, T. V. Manoj, and A. Mukherjee, Opinion formation in time-varying social networks: The case of

A decision network account of reasoning about other people's choices.

PubMed

Jern, Alan; Kemp, Charles

2015-09-01

The ability to predict and reason about other people's choices is fundamental to social interaction. We propose that people reason about other people's choices using mental models that are similar to decision networks. Decision networks are extensions of Bayesian networks that incorporate the idea that choices are made in order to achieve goals. In our first experiment, we explore how people predict the choices of others. Our remaining three experiments explore how people infer the goals and knowledge of others by observing the choices that they make. We show that decision networks account for our data better than alternative computational accounts that do not incorporate the notion of goal-directed choice or that do not rely on probabilistic inference. Copyright © 2015 Elsevier B.V. All rights reserved.

Quantifying the development of user-generated art during 2001–2010

PubMed Central

Yazdani, Mehrdad; Chow, Jay; Manovich, Lev

2017-01-01

One of the main questions in the humanities is how cultures and artistic expressions change over time. While a number of researchers have used quantitative computational methods to study historical changes in literature, music, and cinema, our paper offers the first quantitative analysis of historical changes in visual art created by users of a social online network. We propose a number of computational methods for the analysis of temporal development of art images. We then apply these methods to a sample of 270,000 artworks created between 2001 and 2010 by users of the largest social network for art—DeviantArt (www.deviantart.com). We investigate changes in subjects, techniques, sizes, proportions and also selected visual characteristics of images. Because these artworks are classified by their creators into two general categories—Traditional Art and Digital Art—we are also able to investigate if the use of digital tools has had a significant effect on the content and form of artworks. Our analysis reveals a number of gradual and systematic changes over a ten-year period in artworks belonging to both categories. PMID:28792494

Quantifying the development of user-generated art during 2001-2010.

PubMed

Yazdani, Mehrdad; Chow, Jay; Manovich, Lev

2017-01-01

One of the main questions in the humanities is how cultures and artistic expressions change over time. While a number of researchers have used quantitative computational methods to study historical changes in literature, music, and cinema, our paper offers the first quantitative analysis of historical changes in visual art created by users of a social online network. We propose a number of computational methods for the analysis of temporal development of art images. We then apply these methods to a sample of 270,000 artworks created between 2001 and 2010 by users of the largest social network for art-DeviantArt (www.deviantart.com). We investigate changes in subjects, techniques, sizes, proportions and also selected visual characteristics of images. Because these artworks are classified by their creators into two general categories-Traditional Art and Digital Art-we are also able to investigate if the use of digital tools has had a significant effect on the content and form of artworks. Our analysis reveals a number of gradual and systematic changes over a ten-year period in artworks belonging to both categories.

Toward Psychoinformatics: Computer Science Meets Psychology

PubMed Central

Duke, Éilish; Markowetz, Alexander

2016-01-01

The present paper provides insight into an emerging research discipline called Psychoinformatics. In the context of Psychoinformatics, we emphasize the cooperation between the disciplines of psychology and computer science in handling large data sets derived from heavily used devices, such as smartphones or online social network sites, in order to shed light on a large number of psychological traits, including personality and mood. New challenges await psychologists in light of the resulting “Big Data” sets, because classic psychological methods will only in part be able to analyze this data derived from ubiquitous mobile devices, as well as other everyday technologies. As a consequence, psychologists must enrich their scientific methods through the inclusion of methods from informatics. The paper provides a brief review of one area of this research field, dealing mainly with social networks and smartphones. Moreover, we highlight how data derived from Psychoinformatics can be combined in a meaningful way with data from human neuroscience. We close the paper with some observations of areas for future research and problems that require consideration within this new discipline. PMID:27403204

Use of social network sites and instant messaging does not lead to increased offline social network size, or to emotionally closer relationships with offline network members.

PubMed

Pollet, Thomas V; Roberts, Sam G B; Dunbar, Robin I M

2011-04-01

The effect of Internet use on social relationships is still a matter of intense debate. This study examined the relationships between use of social media (instant messaging and social network sites), network size, and emotional closeness in a sample of 117 individuals aged 18 to 63 years old. Time spent using social media was associated with a larger number of online social network "friends." However, time spent using social media was not associated with larger offline networks, or feeling emotionally closer to offline network members. Further, those that used social media, as compared to non-users of social media, did not have larger offline networks, and were not emotionally closer to offline network members. These results highlight the importance of considering potential time and cognitive constraints on offline social networks when examining the impact of social media use on social relationships.

Winter Simulation Conference, Miami Beach, Fla., December 4-6, 1978, Proceedings. Volumes 1 & 2

NASA Technical Reports Server (NTRS)

Highland, H. J. (Editor); Nielsen, N. R.; Hull, L. G.

1978-01-01

The papers report on the various aspects of simulation such as random variate generation, simulation optimization, ranking and selection of alternatives, model management, documentation, data bases, and instructional methods. Simulation studies in a wide variety of fields are described, including system design and scheduling, government and social systems, agriculture, computer systems, the military, transportation, corporate planning, ecosystems, health care, manufacturing and industrial systems, computer networks, education, energy, production planning and control, financial models, behavioral models, information systems, and inventory control.

Does Not Compute: The High Cost of Low Technology Skills in the U.S.--and What We Can Do about It. Vital Signs: Reports on the Condition of STEM Learning in the U.S.

ERIC Educational Resources Information Center

Change the Equation, 2015

2015-01-01

Although American millennials are the first generation of "digital natives"--that is, people who grew up with computers and the internet--they are not very tech savvy. Using technology for social networking, surfing the web, or taking selfies is a far cry from using it to solve complex problems at work or at home. Truly tech savvy people…

Extracting Social Information from Chemosensory Cues: Consideration of Several Scenarios and Their Functional Implications

PubMed Central

Ben-Shaul, Yoram

2015-01-01

Across all sensory modalities, stimuli can vary along multiple dimensions. Efficient extraction of information requires sensitivity to those stimulus dimensions that provide behaviorally relevant information. To derive social information from chemosensory cues, sensory systems must embed information about the relationships between behaviorally relevant traits of individuals and the distributions of the chemical cues that are informative about these traits. In simple cases, the mere presence of one particular compound is sufficient to guide appropriate behavior. However, more generally, chemosensory information is conveyed via relative levels of multiple chemical cues, in non-trivial ways. The computations and networks needed to derive information from multi-molecule stimuli are distinct from those required by single molecule cues. Our current knowledge about how socially relevant information is encoded by chemical blends, and how it is extracted by chemosensory systems is very limited. This manuscript explores several scenarios and the neuronal computations required to identify them. PMID:26635515

Evaluation of Internet Social Networks using Net scoring Tool: A Case Study in Adverse Drug Reaction Mining.

PubMed

Katsahian, Sandrine; Simond Moreau, Erica; Leprovost, Damien; Lardon, Jeremy; Bousquet, Cedric; Kerdelhué, Gaétan; Abdellaoui, Redhouane; Texier, Nathalie; Burgun, Anita; Boussadi, Abdelali; Faviez, Carole

2015-01-01

Suspected adverse drug reactions (ADR) reported by patients through social media can be a complementary tool to already existing ADRs signal detection processes. However, several studies have shown that the quality of medical information published online varies drastically whatever the health topic addressed. The aim of this study is to use an existing rating tool on a set of social network web sites in order to assess the capabilities of these tools to guide experts for selecting the most adapted social network web site to mine ADRs. First, we reviewed and rated 132 Internet forums and social networks according to three major criteria: the number of visits, the notoriety of the forum and the number of messages posted in relation with health and drug therapy. Second, the pharmacist reviewed the topic-oriented message boards with a small number of drug names to ensure that they were not off topic. Six experts have been chosen to assess the selected internet forums using a French scoring tool: Net scoring. Three different scores and the agreement between experts according to each set of scores using weighted kappa pooled using mean have been computed. Three internet forums were chosen at the end of the selection step. Some criteria get high score (scores 3-4) no matter the website evaluated like accessibility (45-46) or design (34-36), at the opposite some criteria always have bad scores like quantitative (40-42) and ethical aspect (43-44), hyperlinks actualization (30-33). Kappa were positives but very small which corresponds to a weak agreement between experts. The personal opinion of the expert seems to have a major impact, undermining the relevance of the criterion. Our future work is to collect results given by this evaluation grid and proposes a new scoring tool for Internet social networks assessment.

Real-time community detection in full social networks on a laptop

PubMed Central

Chamberlain, Benjamin Paul; Levy-Kramer, Josh; Humby, Clive

2018-01-01

For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph). As global social networks (e.g., Facebook and Twitter) are very large, most approaches make use of distributed computing systems for this purpose. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain. In this article, we present an approach for analyzing full social networks on a standard laptop, allowing for interactive exploration of the communities in the locality of a set of user specified query vertices. The key idea is that the aggregate actions of large numbers of users can be compressed into a data structure that encapsulates the edge weights between vertices in a derived graph. Local communities can be constructed by selecting vertices that are connected to the query vertices with high edge weights in the derived graph. This compression is robust to noise and allows for interactive queries of local communities in real-time, which we define to be less than the average human reaction time of 0.25s. We achieve single-machine real-time performance by compressing the neighborhood of each vertex using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines operating on the full graph to milliseconds on standard laptops. Our method allows exploration of strongly associated regions (i.e., communities) of large graphs in real-time on a laptop. It has been deployed in software that is actively used by social network analysts and offers another channel for media owners to monetize their data, helping them to continue to provide free services that are valued by billions of people globally. PMID:29342158

Modern temporal network theory: a colloquium

NASA Astrophysics Data System (ADS)

Holme, Petter

2015-09-01

The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it is more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.

Social network approaches to recruitment, HIV prevention, medical care, and medication adherence.

PubMed

Latkin, Carl A; Davey-Rothwell, Melissa A; Knowlton, Amy R; Alexander, Kamila A; Williams, Chyvette T; Boodram, Basmattee

2013-06-01

This article reviews the current issues and advancements in social network approaches to HIV prevention and care. Social network analysis can provide a method to understand health disparities in HIV rates, treatment access, and outcomes. Social network analysis is a valuable tool to link social structural factors to individual behaviors. Social networks provide an avenue for low-cost and sustainable HIV prevention interventions that can be adapted and translated into diverse populations. Social networks can be utilized as a viable approach to recruitment for HIV testing and counseling, HIV prevention interventions, optimizing HIV medical care, and medication adherence. Social network interventions may be face-to-face or through social media. Key issues in designing social network interventions are contamination due to social diffusion, network stability, density, and the choice and training of network members. There are also ethical issues involved in the development and implementation of social network interventions. Social network analyses can also be used to understand HIV transmission dynamics.

Breaking the ice and forging links: the importance of socializing in research.

PubMed

Stobbe, Miranda; Mishra, Tarun; Macintyre, Geoff

2013-01-01

When meeting someone for the first time-whether another PhD student, or the Founding Editor-in-chief of PLOS Computational Biology-nothing breaks the ice like eating pancakes or having drinks together. A social atmosphere provides a relaxed, informal environment where people can connect, share ideas, and form collaborations. Being able to build a network and thrive in a social environment is crucial to a successful scientific career. This article highlights the importance of bringing people together who speak the same scientific language in an informal setting. Using examples of events held by Regional Student Groups of the ISCB's Student Council, this article shows that socializing is much more than simply sharing a drink.

Toward Efficient Team Formation for Crowdsourcing in Noncooperative Social Networks.

PubMed

Wang, Wanyuan; Jiang, Jiuchuan; An, Bo; Jiang, Yichuan; Chen, Bing

2017-12-01

Crowdsourcing has become a popular service computing paradigm for requesters to integrate the ubiquitous human-intelligence services for tasks that are difficult for computers but trivial for humans. This paper focuses on crowdsourcing complex tasks by team formation in social networks (SNs) where a requester connects to a large number of workers. A good indicator of efficient team collaboration is the social connection among workers. Most previous social team formation approaches, however, either assume that the requester can maintain information of all workers and can directly communicate with them to build teams, or assume that the workers are cooperative and be willing to join the specific team built by the requester, both of which are impractical in many real situations. To this end, this paper first models each worker as a selfish entity, where the requester prefers to hire inexpensive workers that require less payment and workers prefer to join the profitable teams where they can gain high revenue. Within the noncooperative SNs, a distributed negotiation-based team formation mechanism is designed for the requester to decide which worker to hire and for the worker to decide which team to join and how much should be paid for his skill service provision. The proposed social team formation approach can always build collaborative teams by allowing team members to form a connected graph such that they can work together efficiently. Finally, we conduct a set of experiments on real dataset of workers to evaluate the effectiveness of our approach. The experimental results show that our approach can: 1) preserve considerable social welfare by comparing the benchmark centralized approaches and 2) form the profitable teams within less negotiation time by comparing the traditional distributed approaches, making our approach a more economic option for real-world applications.

Epidemic processes in complex networks

NASA Astrophysics Data System (ADS)

Pastor-Satorras, Romualdo; Castellano, Claudio; Van Mieghem, Piet; Vespignani, Alessandro

2015-07-01

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.

Perceiving nonverbal behavior: neural correlates of processing movement fluency and contingency in dyadic interactions.

PubMed

Georgescu, Alexandra L; Kuzmanovic, Bojana; Santos, Natacha S; Tepest, Ralf; Bente, Gary; Tittgemeyer, Marc; Vogeley, Kai

2014-04-01

Despite the fact that nonverbal dyadic social interactions are abundant in the environment, the neural mechanisms underlying their processing are not yet fully understood. Research in the field of social neuroscience has suggested that two neural networks appear to be involved in social understanding: (1) the action observation network (AON) and (2) the social neural network (SNN). The aim of this study was to determine the differential contributions of the AON and the SNN to the processing of nonverbal behavior as observed in dyadic social interactions. To this end, we used short computer animation sequences displaying dyadic social interactions between two virtual characters and systematically manipulated two key features of movement activity, which are known to influence the perception of meaning in nonverbal stimuli: (1) movement fluency and (2) contingency of movement patterns. A group of 21 male participants rated the "naturalness" of the observed scenes on a four-point scale while undergoing fMRI. Behavioral results showed that both fluency and contingency significantly influenced the "naturalness" experience of the presented animations. Neurally, the AON was preferentially engaged when processing contingent movement patterns, but did not discriminate between different degrees of movement fluency. In contrast, regions of the SNN were engaged more strongly when observing dyads with disturbed movement fluency. In conclusion, while the AON is involved in the general processing of contingent social actions, irrespective of their kinematic properties, the SNN is preferentially recruited when atypical kinematic properties prompt inferences about the agents' intentions. Copyright © 2013 Wiley Periodicals, Inc.

Proactive health computing.

PubMed

Timpka, T

2001-08-01

In an analysis departing from the global health situation, the foundation for a change of paradigm in health informatics based on socially embedded information infrastructures and technologies is identified and discussed. It is shown how an increasing computing and data transmitting capacity can be employed for proactive health computing. As a foundation for ubiquitous health promotion and prevention of disease and injury, proactive health systems use data from multiple sources to supply individuals and communities evidence-based information on means to improve their state of health and avoid health risks. The systems are characterised by: (1) being profusely connected to the world around them, using perceptual interfaces, sensors and actuators; (2) responding to external stimuli at faster than human speeds; (3) networked feedback loops; and (4) humans remaining in control, while being left outside the primary computing loop. The extended scientific mission of this new partnership between computer science, electrical engineering and social medicine is suggested to be the investigation of how the dissemination of information and communication technology on democratic grounds can be made even more important for global health than sanitation and urban planning became a century ago.

Arrhenius-kinetics evidence for quantum tunneling in microbial "social" decision rates.

PubMed

Clark, Kevin B

2010-11-01

Social-like bacteria, fungi and protozoa communicate chemical and behavioral signals to coordinate their specializations into an ordered group of individuals capable of fitter ecological performance. Examples of microbial "social" behaviors include sporulation and dispersion, kin recognition and nonclonal or paired reproduction. Paired reproduction by ciliates is believed to involve intra- and intermate selection through pheromone-stimulated "courting" rituals. Such social maneuvering minimizes survival-reproduction tradeoffs while sorting superior mates from inferior ones, lowering the vertical spread of deleterious genes in geographically constricted populations and possibly promoting advantageous genetic innovations. In a previous article, I reported findings that the heterotrich Spirostomum ambiguum can out-complete mating rivals in simulated social trials by learning behavioral heuristics which it then employs to store and select sets of altruistic and deceptive signaling strategies. Frequencies of strategy use typically follow Maxwell-Boltzmann (MB), Fermi-Dirac (FD) or Bose-Einstein (BE) statistical distributions. For ciliates most adept at social decision making, a brief classical MB computational phase drives signaling behavior into a later quantum BE computational phase that condenses or favors the selection of a single fittest strategy. Appearance of the network analogue of BE condensation coincides with Hebbian-like trial-and-error learning and is consistent with the idea that cells behave as heat engines, where loss of energy associated with specific cellular machinery critical for mating decisions effectively reduces the temperature of intracellular enzymes cohering into weak Fröhlich superposition. I extend these findings by showing the rates at which ciliates switch serial behavioral strategies agree with principles of chemical reactions exhibiting linear and nonlinear Arrhenius kinetics during respective classical and quantum computations. Nonlinear Arrhenius kinetics in ciliate decision making suggest transitions from one signaling strategy to another result from a computational analogue of quantum tunneling in social information processing.

Epidemic modeling in complex realities.

PubMed

Colizza, Vittoria; Barthélemy, Marc; Barrat, Alain; Vespignani, Alessandro

2007-04-01

In our global world, the increasing complexity of social relations and transport infrastructures are key factors in the spread of epidemics. In recent years, the increasing availability of computer power has enabled both to obtain reliable data allowing one to quantify the complexity of the networks on which epidemics may propagate and to envision computational tools able to tackle the analysis of such propagation phenomena. These advances have put in evidence the limits of homogeneous assumptions and simple spatial diffusion approaches, and stimulated the inclusion of complex features and heterogeneities relevant in the description of epidemic diffusion. In this paper, we review recent progresses that integrate complex systems and networks analysis with epidemic modelling and focus on the impact of the various complex features of real systems on the dynamics of epidemic spreading.

Understanding the influence of all nodes in a network

PubMed Central

Lawyer, Glenn

2015-01-01

Centrality measures such as the degree, k-shell, or eigenvalue centrality can identify a network's most influential nodes, but are rarely usefully accurate in quantifying the spreading power of the vast majority of nodes which are not highly influential. The spreading power of all network nodes is better explained by considering, from a continuous-time epidemiological perspective, the distribution of the force of infection each node generates. The resulting metric, the expected force, accurately quantifies node spreading power under all primary epidemiological models across a wide range of archetypical human contact networks. When node power is low, influence is a function of neighbor degree. As power increases, a node's own degree becomes more important. The strength of this relationship is modulated by network structure, being more pronounced in narrow, dense networks typical of social networking and weakening in broader, looser association networks such as the Internet. The expected force can be computed independently for individual nodes, making it applicable for networks whose adjacency matrix is dynamic, not well specified, or overwhelmingly large. PMID:25727453

Fostering Social Engagement and Self-Efficacy in Later Life: Studies with Ubiquitous Computing

NASA Astrophysics Data System (ADS)

Morris, Margaret E.; Lundell, Jay; Dishongh, Terry; Needham, Brad

This chapter describes a multiyear project with a team of social scientists and engineers at Intel focused on emerging technologies and successful aging. Theories of behavioral change are linked to the capabilities of emerging technologies for capturing and reflecting variability in activity and health status. The technologies described in this chapter reflect an attempt to integrate psychological theory and ethnographic research with ubiquitous computing. Ethnographic research that we conducted at the outset of this project consistently underscored the value of social engagement for successful aging. It also pointed out the significant social barriers encountered by many older adults. These barriers - which include changes in lifestyle, mobility, and cognitive functioning - are compounded by a perceived inability to change isolating circumstances. To address these social needs and barriers, we developed a set of prototypes involving sensor networks and feedback displays. This chapter describes the social health technologies that we developed, reactions of the older adults and family caregivers who participated in in-home trials, and implications for future development. We also describe the need for tools to encourage self-awareness and self-efficacy for a broad range of health concerns.

New mothers and media use: associations between blogging, social networking, and maternal well-being.

PubMed

McDaniel, Brandon T; Coyne, Sarah M; Holmes, Erin K

2012-10-01

Drawing on Bronfenbrenner's ecological theory and prior empirical research, the current study examines the way that blogging and social networking may impact feelings of connection and social support, which in turn could impact maternal well-being (e.g., marital functioning, parenting stress, and depression). One hundred and fifty-seven new mothers reported on their media use and various well-being variables. On average, mothers were 27 years old (SD = 5.15) and infants were 7.90 months old (SD = 5.21). All mothers had access to the Internet in their home. New mothers spent approximately 3 hours on the computer each day, with most of this time spent on the Internet. Findings suggested that frequency of blogging predicted feelings of connection to extended family and friends which then predicted perceptions of social support. This in turn predicted maternal well-being, as measured by marital satisfaction, couple conflict, parenting stress, and depression. In sum, blogging may improve new mothers' well-being, as they feel more connected to the world outside their home through the Internet.

Developing Item Response Theory-Based Short Forms to Measure the Social Impact of Burn Injuries.

PubMed

Marino, Molly E; Dore, Emily C; Ni, Pengsheng; Ryan, Colleen M; Schneider, Jeffrey C; Acton, Amy; Jette, Alan M; Kazis, Lewis E

2018-03-01

To develop self-reported short forms for the Life Impact Burn Recovery Evaluation (LIBRE) Profile. Short forms based on the item parameters of discrimination and average difficulty. A support network for burn survivors, peer support networks, social media, and mailings. Burn survivors (N=601) older than 18 years. Not applicable. The LIBRE Profile. Ten-item short forms were developed to cover the 6 LIBRE Profile scales: Relationships with Family & Friends, Social Interactions, Social Activities, Work & Employment, Romantic Relationships, and Sexual Relationships. Ceiling effects were ≤15% for all scales; floor effects were <1% for all scales. The marginal reliability of the short forms ranged from .85 to .89. The LIBRE Profile-Short Forms demonstrated credible psychometric properties. The short form version provides a viable alternative to administering the LIBRE Profile when resources do not allow computer or Internet access. The full item bank, computerized adaptive test, and short forms are all scored along the same metric, and therefore scores are comparable regardless of the mode of administration. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

User Vulnerability and its Reduction on a Social Networking Site

DTIC Science & Technology

2014-01-01

social networking sites bring about new...and explore other users’ profiles and friend networks. Social networking sites have reshaped business models [Vayner- chuk 2009], provided platform... social networking sites is to enable users to be more social, user privacy and security issues cannot be ignored. On one hand, most social networking sites

Culture and Demography: From Reluctant Bedfellows to Committed Partners

PubMed Central

Bachrach, Christine A.

2015-01-01

Demography and culture have had a long but ambivalent relationship. Cultural influences are widely recognized as important for demographic outcomes, but are often “backgrounded” in demographic research. I argue that progress towards a more successful integration is feasible and suggest a network model of culture as a potential tool. The network model bridges both traditional (holistic and institutional) and contemporary (tool kit) models of culture used in the social sciences and offers a simple vocabulary for the diverse set of cultural concepts such as attitudes, beliefs and norms, and quantitative measures of how culture is organized. The proposed model conceptualizes culture as a nested network of meanings which are represented by schemas that range in complexity from simple concepts to multifaceted cultural models. I illustrate the potential value of a model using accounts of the cultural changes underpinning the transformation of marriage in the U.S. and point to developments in the social, cognitive and computational sciences that could facilitate the application of the model in empirical demographic research. PMID:24338643

Culture and demography: from reluctant bedfellows to committed partners.

PubMed

Bachrach, Christine A

2014-02-01

Demography and culture have had a long but ambivalent relationship. Cultural influences are widely recognized as important for demographic outcomes but are often "backgrounded" in demographic research. I argue that progress toward a more successful integration is feasible and suggest a network model of culture as a potential tool. The network model bridges both traditional (holistic and institutional) and contemporary (tool kit) models of culture used in the social sciences and offers a simple vocabulary for a diverse set of cultural concepts, such as attitudes, beliefs, and norms, as well as quantitative measures of how culture is organized. The proposed model conceptualizes culture as a nested network of meanings represented by schemas that range in complexity from simple concepts to multifaceted cultural models. I illustrate the potential value of a model using accounts of the cultural changes underpinning the transformation of marriage in the United States and point to developments in the social, cognitive, and computational sciences that could facilitate the application of the model in empirical demographic research.

Awareness of Facebook Education among Student Teachers in Present Scenario

ERIC Educational Resources Information Center

Joan, Robert

2015-01-01

Facebook is one of the most popular social networks in the world. Day by day, it is becoming an indispensable part of people lives affecting the daily routine of people. Developments in Computer Technology lead to developments in Communication Technologies and styles as well. Facebook is one of the significant examples of these developments. In…

E-Learning 3.0 = E-Learning 2.0 + Web 3.0?

ERIC Educational Resources Information Center

Hussain, Fehmida

2012-01-01

Web 3.0, termed as the semantic web or the web of data is the transformed version of Web 2.0 with technologies and functionalities such as intelligent collaborative filtering, cloud computing, big data, linked data, openness, interoperability and smart mobility. If Web 2.0 is about social networking and mass collaboration between the creator and…

Technology Uses in Campus Activism from 2000 to 2008: Implications for Civic Learning

ERIC Educational Resources Information Center

Biddix, J. Patrick

2010-01-01

This qualitative study examines use of information and communication technologies (ICTs) such as computers, cell phones, text messaging, and social networking sites, for campus activism. Participants were 22 student leaders representing eight campuses from 2000 to 2008. The focus of this study was two-fold: first, to describe the form and function…

The Evolving Roles of Language Teachers: Trained Coders, Local Researchers, Global Citizens

ERIC Educational Resources Information Center

Godwin-Jones, Robert

2015-01-01

Language teachers are working in a world which has changed in the past decades in fundamentally disruptive ways, through profound changes in the role that networked computers play in everyday life and through the social and demographic shifts brought on by an increasingly globalized society, bringing together more than ever before people from…

Shortfall online: The development of an educational computer game for teaching sustainable engineering to Millennial Generation students

NASA Astrophysics Data System (ADS)

Gennett, Zachary Andrew

Millennial Generation students bring significant learning and teaching challenges to the classroom, because of their unique learning styles, breadth of interests related to social and environmental issues, and intimate experiences with technology. As a result, there has been an increased willingness at many universities to experiment with pedagogical strategies that depart from a traditional "learning by listening" model, and move toward more innovative methods involving active learning through computer games. In particular, current students typically express a strong interest in sustainability in which economic concerns must be weighed relative to environmental and social responsibilities. A game-based setting could prove very effective for fostering an operational understanding of these tradeoffs, and especially the social dimension which remains largely underdeveloped relative to the economic and environmental aspects. Through an examination of the educational potential of computer games, this study hypothesizes that to acquire the skills necessary to manage and understand the complexities of sustainability, Millennial Generation students must be engaged in active learning exercises that present dynamic problems and foster a high level of social interaction. This has led to the development of an educational computer game, entitled Shortfall, which simulates a business milieu for testing alternative paths regarding the principles of sustainability. This study examines the evolution of Shortfall from an educational board game that teaches the principles of environmentally benign manufacturing, to a completely networked computer game, entitled Shortfall Online that teaches the principles of sustainability. A capital-based theory of sustainability is adopted to more accurately convey the tradeoffs and opportunity costs among economic prosperity, environmental preservation, and societal responsibilities. While the economic and environmental aspects of sustainability have received considerable attention in traditional pedagogical approaches, specific focus is provided for the social dimension of sustainability, as it had remained largely underdeveloped. To measure social sustainability and provide students with an understanding of its significance, a prospective metric utilizing a social capital peer-evaluation survey, unique to Shortfall, is developed.

Social Networks, Social Circles, and Job Satisfaction.

ERIC Educational Resources Information Center

Hurlbert, Jeanne S.

1991-01-01

Tests the hypothesis that social networks serve as a social resource that effects job satisfaction through the provision of social support. Argues that three types of networks are likely to affect job satisfaction: dense networks, social circles composed of co-workers, and kin-centered networks. (JOW)

A Sensemaking Approach to Visual Analytics of Attribute-Rich Social Networks

ERIC Educational Resources Information Center

Gou, Liang

2012-01-01

Social networks have become more complex, in particular considering the fact that elements in social networks are not only abstract topological nodes and links, but contain rich social attributes and reflecting diverse social relationships. For example, in a co-authorship social network in a scientific community, nodes in the social network, which…

Game-Based Virtual Worlds as Decentralized Virtual Activity Systems

NASA Astrophysics Data System (ADS)

Scacchi, Walt

There is widespread interest in the development and use of decentralized systems and virtual world environments as possible new places for engaging in collaborative work activities. Similarly, there is widespread interest in stimulating new technological innovations that enable people to come together through social networking, file/media sharing, and networked multi-player computer game play. A decentralized virtual activity system (DVAS) is a networked computer supported work/play system whose elements and social activities can be both virtual and decentralized (Scacchi et al. 2008b). Massively multi-player online games (MMOGs) such as World of Warcraft and online virtual worlds such as Second Life are each popular examples of a DVAS. Furthermore, these systems are beginning to be used for research, deve-lopment, and education activities in different science, technology, and engineering domains (Bainbridge 2007, Bohannon et al. 2009; Rieber 2005; Scacchi and Adams 2007; Shaffer 2006), which are also of interest here. This chapter explores two case studies of DVASs developed at the University of California at Irvine that employ game-based virtual worlds to support collaborative work/play activities in different settings. The settings include those that model and simulate practical or imaginative physical worlds in different domains of science, technology, or engineering through alternative virtual worlds where players/workers engage in different kinds of quests or quest-like workflows (Jakobsson 2006).

Community detection using preference networks

NASA Astrophysics Data System (ADS)

Tasgin, Mursel; Bingol, Haluk O.

2018-04-01

Community detection is the task of identifying clusters or groups of nodes in a network where nodes within the same group are more connected with each other than with nodes in different groups. It has practical uses in identifying similar functions or roles of nodes in many biological, social and computer networks. With the availability of very large networks in recent years, performance and scalability of community detection algorithms become crucial, i.e. if time complexity of an algorithm is high, it cannot run on large networks. In this paper, we propose a new community detection algorithm, which has a local approach and is able to run on large networks. It has a simple and effective method; given a network, algorithm constructs a preference network of nodes where each node has a single outgoing edge showing its preferred node to be in the same community with. In such a preference network, each connected component is a community. Selection of the preferred node is performed using similarity based metrics of nodes. We use two alternatives for this purpose which can be calculated in 1-neighborhood of nodes, i.e. number of common neighbors of selector node and its neighbors and, the spread capability of neighbors around the selector node which is calculated by the gossip algorithm of Lind et.al. Our algorithm is tested on both computer generated LFR networks and real-life networks with ground-truth community structure. It can identify communities accurately in a fast way. It is local, scalable and suitable for distributed execution on large networks.

New Markov-Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks.

PubMed

Riera-Fernández, Pablo; Munteanu, Cristian R; Escobar, Manuel; Prado-Prado, Francisco; Martín-Romalde, Raquel; Pereira, David; Villalba, Karen; Duardo-Sánchez, Aliuska; González-Díaz, Humberto

2012-01-21

Graph and Complex Network theory is expanding its application to different levels of matter organization such as molecular, biological, technological, and social networks. A network is a set of items, usually called nodes, with connections between them, which are called links or edges. There are many different experimental and/or theoretical methods to assign node-node links depending on the type of network we want to construct. Unfortunately, the use of a method for experimental reevaluation of the entire network is very expensive in terms of time and resources; thus the development of cheaper theoretical methods is of major importance. In addition, different methods to link nodes in the same type of network are not totally accurate in such a way that they do not always coincide. In this sense, the development of computational methods useful to evaluate connectivity quality in complex networks (a posteriori of network assemble) is a goal of major interest. In this work, we report for the first time a new method to calculate numerical quality scores S(L(ij)) for network links L(ij) (connectivity) based on the Markov-Shannon Entropy indices of order k-th (θ(k)) for network nodes. The algorithm may be summarized as follows: (i) first, the θ(k)(j) values are calculated for all j-th nodes in a complex network already constructed; (ii) A Linear Discriminant Analysis (LDA) is used to seek a linear equation that discriminates connected or linked (L(ij)=1) pairs of nodes experimentally confirmed from non-linked ones (L(ij)=0); (iii) the new model is validated with external series of pairs of nodes; (iv) the equation obtained is used to re-evaluate the connectivity quality of the network, connecting/disconnecting nodes based on the quality scores calculated with the new connectivity function. This method was used to study different types of large networks. The linear models obtained produced the following results in terms of overall accuracy for network reconstruction: Metabolic networks (72.3%), Parasite-Host networks (93.3%), CoCoMac brain cortex co-activation network (89.6%), NW Spain fasciolosis spreading network (97.2%), Spanish financial law network (89.9%) and World trade network for Intelligent & Active Food Packaging (92.8%). In order to seek these models, we studied an average of 55,388 pairs of nodes in each model and a total of 332,326 pairs of nodes in all models. Finally, this method was used to solve a more complicated problem. A model was developed to score the connectivity quality in the Drug-Target network of US FDA approved drugs. In this last model the θ(k) values were calculated for three types of molecular networks representing different levels of organization: drug molecular graphs (atom-atom bonds), protein residue networks (amino acid interactions), and drug-target network (compound-protein binding). The overall accuracy of this model was 76.3%. This work opens a new door to the computational reevaluation of network connectivity quality (collation) for complex systems in molecular, biomedical, technological, and legal-social sciences as well as in world trade and industry. Copyright © 2011 Elsevier Ltd. All rights reserved.

Semantic Networks and Social Networks

ERIC Educational Resources Information Center

Downes, Stephen

2005-01-01

Purpose: To illustrate the need for social network metadata within semantic metadata. Design/methodology/approach: Surveys properties of social networks and the semantic web, suggests that social network analysis applies to semantic content, argues that semantic content is more searchable if social network metadata is merged with semantic web…

Computational communities: African-American cultural capital in computer science education

NASA Astrophysics Data System (ADS)

Lachney, Michael

2017-10-01

Enrolling the cultural capital of underrepresented communities in PK-12 technology and curriculum design has been a primary strategy for broadening the participation of students of color in U.S. computer science (CS) fields. This article examines two ways that African-American cultural capital and computing can be bridged in CS education. The first is community representation, using cultural capital to highlight students' social identities and networks through computational thinking. The second, computational integration, locates computation in cultural capital itself. I survey two risks - the appearance of shallow computing and the reproduction of assimilationist logics - that may arise when constructing one bridge without the other. To avoid these risks, I introduce the concept of computational communities by exploring areas in CS education that employ both strategies. This concept is then grounded in qualitative data from an after school program that connected CS to African-American cosmetology.

Predicting Human Preferences Using the Block Structure of Complex Social Networks

PubMed Central

Guimerà, Roger; Llorente, Alejandro; Moro, Esteban; Sales-Pardo, Marta

2012-01-01

With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a “new” computational social science. Here, we propose a novel approach based on stochastic block models, which have been developed by sociologists as plausible models of complex networks of social interactions. Our model is in the spirit of predicting individuals' preferences based on the preferences of others but, rather than fitting a particular model, we rely on a Bayesian approach that samples over the ensemble of all possible models. We show that our approach is considerably more accurate than leading recommender algorithms, with major relative improvements between 38% and 99% over industry-level algorithms. Besides, our approach sheds light on decision-making processes by identifying groups of individuals that have consistently similar preferences, and enabling the analysis of the characteristics of those groups. PMID:22984533

An examination of the relationship between athlete leadership and cohesion using social network analysis.

PubMed

Loughead, Todd M; Fransen, Katrien; Van Puyenbroeck, Stef; Hoffmann, Matt D; De Cuyper, Bert; Vanbeselaere, Norbert; Boen, Filip

2016-11-01

Two studies investigated the structure of different athlete leadership networks and its relationship to cohesion using social network analysis. In Study 1, we examined the relationship between a general leadership quality network and task and social cohesion as measured by the Group Environment Questionnaire (GEQ). In Study 2, we investigated the leadership networks for four different athlete leadership roles (task, motivational, social and external) and their association with task and social cohesion networks. In Study 1, the results demonstrated that the general leadership quality network was positively related to task and social cohesion. The results from Study 2 indicated positive correlations between the four leadership networks and task and social cohesion networks. Further, the motivational leadership network emerged as the strongest predictor of the task cohesion network, while the social leadership network was the strongest predictor of the social cohesion network. The results complement a growing body of research indicating that athlete leadership has a positive association with cohesion.

Discovering latent commercial networks from online financial news articles

NASA Astrophysics Data System (ADS)

Xia, Yunqing; Su, Weifeng; Lau, Raymond Y. K.; Liu, Yi

2013-08-01

Unlike most online social networks where explicit links among individual users are defined, the relations among commercial entities (e.g. firms) may not be explicitly declared in commercial Web sites. One main contribution of this article is the development of a novel computational model for the discovery of the latent relations among commercial entities from online financial news. More specifically, a CRF model which can exploit both structural and contextual features is applied to commercial entity recognition. In addition, a point-wise mutual information (PMI)-based unsupervised learning method is developed for commercial relation identification. To evaluate the effectiveness of the proposed computational methods, a prototype system called CoNet has been developed. Based on the financial news articles crawled from Google finance, the CoNet system achieves average F-scores of 0.681 and 0.754 in commercial entity recognition and commercial relation identification, respectively. Our experimental results confirm that the proposed shallow natural language processing methods are effective for the discovery of latent commercial networks from online financial news.

Some scale-free networks could be robust under selective node attacks

NASA Astrophysics Data System (ADS)

Zheng, Bojin; Huang, Dan; Li, Deyi; Chen, Guisheng; Lan, Wenfei

2011-04-01

It is a mainstream idea that scale-free network would be fragile under the selective attacks. Internet is a typical scale-free network in the real world, but it never collapses under the selective attacks of computer viruses and hackers. This phenomenon is different from the deduction of the idea above because this idea assumes the same cost to delete an arbitrary node. Hence this paper discusses the behaviors of the scale-free network under the selective node attack with different cost. Through the experiments on five complex networks, we show that the scale-free network is possibly robust under the selective node attacks; furthermore, the more compact the network is, and the larger the average degree is, then the more robust the network is; with the same average degrees, the more compact the network is, the more robust the network is. This result would enrich the theory of the invulnerability of the network, and can be used to build robust social, technological and biological networks, and also has the potential to find the target of drugs.

Applications of Social Network Analysis

NASA Astrophysics Data System (ADS)

Thilagam, P. Santhi

A social network [2] is a description of the social structure between actors, mostly persons, groups or organizations. It indicates the ways in which they are connected with each other by some relationship such as friendship, kinship, finance exchange etc. In a nutshell, when the person uses already known/unknown people to create new contacts, it forms social networking. The social network is not a new concept rather it can be formed when similar people interact with each other directly or indirectly to perform particular task. Examples of social networks include a friendship networks, collaboration networks, co-authorship networks, and co-employees networks which depict the direct interaction among the people. There are also other forms of social networks, such as entertainment networks, business Networks, citation networks, and hyperlink networks, in which interaction among the people is indirect. Generally, social networks operate on many levels, from families up to the level of nations and assists in improving interactive knowledge sharing, interoperability and collaboration.

ERMHAN: A Context-Aware Service Platform to Support Continuous Care Networks for Home-Based Assistance

PubMed Central

Paganelli, Federica; Spinicci, Emilio; Giuli, Dino

2008-01-01

Continuous care models for chronic diseases pose several technology-oriented challenges for home-based continuous care, where assistance services rely on a close collaboration among different stakeholders such as health operators, patient relatives, and social community members. Here we describe Emilia Romagna Mobile Health Assistance Network (ERMHAN) a multichannel context-aware service platform designed to support care networks in cooperating and sharing information with the goal of improving patient quality of life. In order to meet extensibility and flexibility requirements, this platform has been developed through ontology-based context-aware computing and a service oriented approach. We also provide some preliminary results of performance analysis and user survey activity. PMID:18695739

A community detection algorithm based on structural similarity

NASA Astrophysics Data System (ADS)

Guo, Xuchao; Hao, Xia; Liu, Yaqiong; Zhang, Li; Wang, Lu

2017-09-01

In order to further improve the efficiency and accuracy of community detection algorithm, a new algorithm named SSTCA (the community detection algorithm based on structural similarity with threshold) is proposed. In this algorithm, the structural similarities are taken as the weights of edges, and the threshold k is considered to remove multiple edges whose weights are less than the threshold, and improve the computational efficiency. Tests were done on the Zachary’s network, Dolphins’ social network and Football dataset by the proposed algorithm, and compared with GN and SSNCA algorithm. The results show that the new algorithm is superior to other algorithms in accuracy for the dense networks and the operating efficiency is improved obviously.

Social Networks and Participation with Others for Youth with Learning, Attention and Autism Spectrum Disorders

PubMed Central

Kreider, Consuelo M.; Bendixen, Roxanna M.; Young, Mary Ellen; Prudencio, Stephanie M.; McCarty, Christopher; Mann, William C.

2015-01-01

Background Social participation involves activities and roles providing interactions with others, including those within their social networks. Purpose Characterize social networks and participation with others for 36 adolescents, ages 11-16 years, with (n = 19) and without (n = 17) learning disability, attention disorder or high-functioning autism. Methods Social networks were measured using methods of personal network analysis. The Children's Assessment of Participation and Enjoyment With Whom dimension scores was used to measure participation with others. Youth from the clinical group were interviewed regarding their experiences within their social networks. Findings Group differences were observed for six social network variables and in the proportion of overall, physical, recreational, social and informal activities engaged with family and/or friends. Qualitative findings explicated strategies used in building, shaping and maintaining their social networks. Implications Social network factors should be considered when seeking to understand social participation. PMID:26755040

Gamification and serious games for personalized health.

PubMed

McCallum, Simon

2012-01-01

Computer games are no longer just a trivial activity played by children in arcades. Social networking and casual gaming have broadened the market for, and acceptance of, games. This has coincided with a realization of their power to engage and motivate players. Good computer games are excellent examples of modern educational theory [1]. The military, health providers, governments, and educators, all use computer games. This paper focuses on Games for Health, discussing the range of areas and approaches to developing these games. We extend a taxonomy for Games for Health, describe a case study on games for dementia sufferers, and finally, present some challenges and research opportunities in this area.

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.

Breaking the Ice and Forging Links: The Importance of Socializing in Research

PubMed Central

Stobbe, Miranda; Mishra, Tarun; Macintyre, Geoff

2013-01-01

When meeting someone for the first time—whether another PhD student, or the Founding Editor-in-chief of PLOS Computational Biology—nothing breaks the ice like eating pancakes or having drinks together. A social atmosphere provides a relaxed, informal environment where people can connect, share ideas, and form collaborations. Being able to build a network and thrive in a social environment is crucial to a successful scientific career. This article highlights the importance of bringing people together who speak the same scientific language in an informal setting. Using examples of events held by Regional Student Groups of the ISCB's Student Council, this article shows that socializing is much more than simply sharing a drink. PMID:24282392

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

PubMed Central

Vadillo, Miguel A; Curcin, Vasa

2017-01-01

Background Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose. Objective The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research. Methods We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals. Results The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis. Conclusions Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques. PMID:28663166

Sensible organizations: technology and methodology for automatically measuring organizational behavior.

PubMed

Olguin Olguin, Daniel; Waber, Benjamin N; Kim, Taemie; Mohan, Akshay; Ara, Koji; Pentland, Alex

2009-02-01

We present the design, implementation, and deployment of a wearable computing platform for measuring and analyzing human behavior in organizational settings. We propose the use of wearable electronic badges capable of automatically measuring the amount of face-to-face interaction, conversational time, physical proximity to other people, and physical activity levels in order to capture individual and collective patterns of behavior. Our goal is to be able to understand how patterns of behavior shape individuals and organizations. By using on-body sensors in large groups of people for extended periods of time in naturalistic settings, we have been able to identify, measure, and quantify social interactions, group behavior, and organizational dynamics. We deployed this wearable computing platform in a group of 22 employees working in a real organization over a period of one month. Using these automatic measurements, we were able to predict employees' self-assessments of job satisfaction and their own perceptions of group interaction quality by combining data collected with our platform and e-mail communication data. In particular, the total amount of communication was predictive of both of these assessments, and betweenness in the social network exhibited a high negative correlation with group interaction satisfaction. We also found that physical proximity and e-mail exchange had a negative correlation of r = -0.55 (p 0.01), which has far-reaching implications for past and future research on social networks.

Dynamic Communicability Predicts Infectiousness

NASA Astrophysics Data System (ADS)

Mantzaris, Alexander V.; Higham, Desmond J.

Using real, time-dependent social interaction data, we look at correlations between some recently proposed dynamic centrality measures and summaries from large-scale epidemic simulations. The evolving network arises from email exchanges. The centrality measures, which are relatively inexpensive to compute, assign rankings to individual nodes based on their ability to broadcast information over the dynamic topology. We compare these with node rankings based on infectiousness that arise when a full stochastic SI simulation is performed over the dynamic network. More precisely, we look at the proportion of the network that a node is able to infect over a fixed time period, and the length of time that it takes for a node to infect half the network. We find that the dynamic centrality measures are an excellent, and inexpensive, proxy for the full simulation-based measures.

Neural and cortisol responses during play with human and computer partners in children with autism

PubMed Central

Edmiston, Elliot Kale; Merkle, Kristen

2015-01-01

Children with autism spectrum disorder (ASD) exhibit impairment in reciprocal social interactions, including play, which can manifest as failure to show social preference or discrimination between social and nonsocial stimuli. To explore mechanisms underlying these deficits, we collected salivary cortisol from 42 children 8–12 years with ASD or typical development during a playground interaction with a confederate child. Participants underwent functional MRI during a prisoner’s dilemma game requiring cooperation or defection with a human (confederate) or computer partner. Search region of interest analyses were based on previous research (e.g. insula, amygdala, temporal parietal junction—TPJ). There were significant group differences in neural activation based on partner and response pattern. When playing with a human partner, children with ASD showed limited engagement of a social salience brain circuit during defection. Reduced insula activation during defection in the ASD children relative to TD children, regardless of partner type, was also a prominent finding. Insula and TPJ BOLD during defection was also associated with stress responsivity and behavior in the ASD group under playground conditions. Children with ASD engage social salience networks less than TD children during conditions of social salience, supporting a fundamental disturbance of social engagement. PMID:25552572

Neural and cortisol responses during play with human and computer partners in children with autism.

PubMed

Edmiston, Elliot Kale; Merkle, Kristen; Corbett, Blythe A

2015-08-01

Children with autism spectrum disorder (ASD) exhibit impairment in reciprocal social interactions, including play, which can manifest as failure to show social preference or discrimination between social and nonsocial stimuli. To explore mechanisms underlying these deficits, we collected salivary cortisol from 42 children 8-12 years with ASD or typical development during a playground interaction with a confederate child. Participants underwent functional MRI during a prisoner's dilemma game requiring cooperation or defection with a human (confederate) or computer partner. Search region of interest analyses were based on previous research (e.g. insula, amygdala, temporal parietal junction-TPJ). There were significant group differences in neural activation based on partner and response pattern. When playing with a human partner, children with ASD showed limited engagement of a social salience brain circuit during defection. Reduced insula activation during defection in the ASD children relative to TD children, regardless of partner type, was also a prominent finding. Insula and TPJ BOLD during defection was also associated with stress responsivity and behavior in the ASD group under playground conditions. Children with ASD engage social salience networks less than TD children during conditions of social salience, supporting a fundamental disturbance of social engagement. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Social networks, social support and psychiatric symptoms: social determinants and associations within a multicultural community population.

PubMed

Smyth, Natasha; Siriwardhana, Chesmal; Hotopf, Matthew; Hatch, Stephani L

2015-07-01

Little is known about how social networks and social support are distributed within diverse communities and how different types of each are associated with a range of psychiatric symptoms. This study aims to address such shortcomings by: (1) describing the demographic and socioeconomic characteristics of social networks and social support in a multicultural population and (2) examining how each is associated with multiple mental health outcomes. Data is drawn from the South East London Community Health Study; a cross-sectional study of 1,698 adults conducted between 2008 and 2010. The findings demonstrate variation in social networks and social support by socio-demographic factors. Ethnic minority groups reported larger family networks but less perceived instrumental support. Older individuals and migrant groups reported lower levels of particular network and support types. Individuals from lower socioeconomic groups tended to report less social networks and support across the indicators measured. Perceived emotional and instrumental support, family and friend network size emerged as protective factors for common mental disorder, personality dysfunction and psychotic experiences. In contrast, both social networks and social support appear less relevant for hazardous alcohol use. The findings both confirm established knowledge that social networks and social support exert differential effects on mental health and furthermore suggest that the particular type of social support may be important. In contrast, different types of social network appear to impact upon poor mental health in a more uniform way. Future psychosocial strategies promoting mental health should consider which social groups are vulnerable to reduced social networks and poor social support and which diagnostic groups may benefit most.

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…

Identifying influencers from sampled social networks

NASA Astrophysics Data System (ADS)

Tsugawa, Sho; Kimura, Kazuma

2018-10-01

Identifying influencers who can spread information to many other individuals from a social network is a fundamental research task in the network science research field. Several measures for identifying influencers have been proposed, and the effectiveness of these influence measures has been evaluated for the case where the complete social network structure is known. However, it is difficult in practice to obtain the complete structure of a social network because of missing data, false data, or node/link sampling from the social network. In this paper, we investigate the effects of node sampling from a social network on the effectiveness of influence measures at identifying influencers. Our experimental results show that the negative effect of biased sampling, such as sample edge count, on the identification of influencers is generally small. For social media networks, we can identify influencers whose influence is comparable with that of those identified from the complete social networks by sampling only 10%-30% of the networks. Moreover, our results also suggest the possible benefit of network sampling in the identification of influencers. Our results show that, for some networks, nodes with higher influence can be discovered from sampled social networks than from complete social networks.

Social media users have different experiences, motivations, and quality of life.

PubMed

Campisi, Jay; Folan, Denis; Diehl, Grace; Kable, Timothy; Rademeyer, Candice

2015-08-30

While the number of individuals participating in internet-based social networks has continued to rise, it is unclear how participating in social networks might influence quality of life (QOL). Individuals differ in their experiences, motivations for, and amount of time using internet-based social networks, therefore, we examined if individuals differing in social network user experiences, motivations and frequency of social network also differed in self-reported QOL. Two-hundred and thirty-seven individuals (aged 18-65) were recruited online using the online platform Mechanical Turk (MTurk). All participants completed a web-based survey examining social network use and the World Health Organization Quality of Life Scale Abbreviated Version (WHOQOL-Bref) to assess QOL. Individuals who reported positive associations with the use of social networks demonstrated higher QOL while those reporting negative associates demonstrated lower QOL. Moreover, individuals using social networks to stay connected to friends demonstrated higher QOL while those using social networking for dating purposes reported lower QOL. Frequency of social network use did not relate to QOL. These results suggest that QOL differs among social network users. Thus, participating in social networking may be a way to either promote or detract from QOL. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

The theory of reasoned action as parallel constraint satisfaction: towards a dynamic computational model of health behavior.

PubMed

Orr, Mark G; Thrush, Roxanne; Plaut, David C

2013-01-01

The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual's pre-existing belief structure and the beliefs of others in the individual's social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics.

The Theory of Reasoned Action as Parallel Constraint Satisfaction: Towards a Dynamic Computational Model of Health Behavior

PubMed Central

Orr, Mark G.; Thrush, Roxanne; Plaut, David C.

2013-01-01

The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual’s pre-existing belief structure and the beliefs of others in the individual’s social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics. PMID:23671603

An Open Architecture to Support Social and Health Services in a Smart TV Environment.

PubMed

Costa, Carlos Rivas; Anido-Rifon, Luis E; Fernandez-Iglesias, Manuel J

2017-03-01

To design, implement, and test a solution to provide social and health services for the elderly at home based on smart TV technologies and access to all services. The architecture proposed is based on an open software platform and standard personal computing hardware. This provides great flexibility to develop new applications over the underlying infrastructure or to integrate new devices, for instance to monitor a broad range of vital signs in those cases where home monitoring is required. An actual system as a proof-of-concept was designed, implemented, and deployed. Applications range from social network clients to vital signs monitoring; from interactive TV contests to conventional online care applications such as medication reminders or telemedicine. In both cases, the results have been very positive, confirming the initial perception of the TV as a convenient, easy-to-use technology to provide social and health care. The TV set is a much more familiar computing interface for most senior users, and as a consequence, smart TVs become a most convenient solution for the design and implementation of applications and services targeted to this user group. This proposal has been tested in real setting with 62 senior people at their homes. Users included both individuals with experience using computers and others reluctant to them.

The Analysis of Duocentric Social Networks: A Primer.

PubMed

Kennedy, David P; Jackson, Grace L; Green, Harold D; Bradbury, Thomas N; Karney, Benjamin R

2015-02-01

Marriages and other intimate partnerships are facilitated or constrained by the social networks within which they are embedded. To date, methods used to assess the social networks of couples have been limited to global ratings of social network characteristics or network data collected from each partner separately. In the current article, the authors offer new tools for expanding on the existing literature by describing methods of collecting and analyzing duocentric social networks, that is, the combined social networks of couples. They provide an overview of the key considerations for measuring duocentric networks, such as how and why to combine separate network interviews with partners into one shared duocentric network, the number of network members to assess, and the implications of different network operationalizations. They illustrate these considerations with analyses of social network data collected from 57 low-income married couples, presenting visualizations and quantitative measures of network composition and structure.

Virtualization in education: Information Security lab in your hands

NASA Astrophysics Data System (ADS)

Karlov, A. A.

2016-09-01

The growing demand for qualified specialists in advanced information technologies poses serious challenges to the education and training of young personnel for science, industry and social problems. Virtualization as a way to isolate the user from the physical characteristics of computing resources (processors, servers, operating systems, networks, applications, etc.), has, in particular, an enormous influence in the field of education, increasing its efficiency, reducing the cost, making it more widely and readily available. The study of Information Security of computer systems is considered as an example of use of virtualization in education.

Emergency navigation without an infrastructure.

PubMed

Gelenbe, Erol; Bi, Huibo

2014-08-18

Emergency navigation systems for buildings and other built environments, such as sport arenas or shopping centres, typically rely on simple sensor networks to detect emergencies and, then, provide automatic signs to direct the evacuees. The major drawbacks of such static wireless sensor network (WSN)-based emergency navigation systems are the very limited computing capacity, which makes adaptivity very difficult, and the restricted battery power, due to the low cost of sensor nodes for unattended operation. If static wireless sensor networks and cloud-computing can be integrated, then intensive computations that are needed to determine optimal evacuation routes in the presence of time-varying hazards can be offloaded to the cloud, but the disadvantages of limited battery life-time at the client side, as well as the high likelihood of system malfunction during an emergency still remain. By making use of the powerful sensing ability of smart phones, which are increasingly ubiquitous, this paper presents a cloud-enabled indoor emergency navigation framework to direct evacuees in a coordinated fashion and to improve the reliability and resilience for both communication and localization. By combining social potential fields (SPF) and a cognitive packet network (CPN)-based algorithm, evacuees are guided to exits in dynamic loose clusters. Rather than relying on a conventional telecommunications infrastructure, we suggest an ad hoc cognitive packet network (AHCPN)-based protocol to adaptively search optimal communication routes between portable devices and the network egress nodes that provide access to cloud servers, in a manner that spares the remaining battery power of smart phones and minimizes the time latency. Experimental results through detailed simulations indicate that smart human motion and smart network management can increase the survival rate of evacuees and reduce the number of drained smart phones in an evacuation process.

Emergency Navigation without an Infrastructure

PubMed Central

Gelenbe, Erol; Bi, Huibo

2014-01-01

Emergency navigation systems for buildings and other built environments, such as sport arenas or shopping centres, typically rely on simple sensor networks to detect emergencies and, then, provide automatic signs to direct the evacuees. The major drawbacks of such static wireless sensor network (WSN)-based emergency navigation systems are the very limited computing capacity, which makes adaptivity very difficult, and the restricted battery power, due to the low cost of sensor nodes for unattended operation. If static wireless sensor networks and cloud-computing can be integrated, then intensive computations that are needed to determine optimal evacuation routes in the presence of time-varying hazards can be offloaded to the cloud, but the disadvantages of limited battery life-time at the client side, as well as the high likelihood of system malfunction during an emergency still remain. By making use of the powerful sensing ability of smart phones, which are increasingly ubiquitous, this paper presents a cloud-enabled indoor emergency navigation framework to direct evacuees in a coordinated fashion and to improve the reliability and resilience for both communication and localization. By combining social potential fields (SPF) and a cognitive packet network (CPN)-based algorithm, evacuees are guided to exits in dynamic loose clusters. Rather than relying on a conventional telecommunications infrastructure, we suggest an ad hoc cognitive packet network (AHCPN)-based protocol to adaptively search optimal communication routes between portable devices and the network egress nodes that provide access to cloud servers, in a manner that spares the remaining battery power of smart phones and minimizes the time latency. Experimental results through detailed simulations indicate that smart human motion and smart network management can increase the survival rate of evacuees and reduce the number of drained smart phones in an evacuation process. PMID:25196014

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

Collective dynamics of 'small-world' networks.

PubMed

Watts, D J; Strogatz, S H

1998-06-04

Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

Prospects for the Use of Social Media Marketing Instruments in Health Promotion by Polish Marshal Offices.

PubMed

Syrkiewicz-S Witała, Magdalena; Romaniuk, Piotr; Strzelecka, Agnieszka; Lar, Katarzyna; Holecki, Tomasz

2018-01-01

To investigate whether the Polish Marshal Offices use instruments for social media marketing activities in the field of health promotion. 14 Polish Marshal Offices participated. The Computer-Assisted Web Interview and Computer-Assisted Telephone Interview were used along with a proprietary questionnaire. Standard statistical methods were employed. The number of people using the Internet and social media in Poland is steadily growing. The majority of the offices (93%) performed health promotion activities. The authorities collaborated with other units of local government and non-governmental organizations in these activities. According to respondents, the most convincing form of health promotion is direct communication (46%). More than half of the surveyed offices (56%) did not use portals or social networking sites in health campaigns. The rest of the offices indicated using Facebook (25%) or YouTube (6%). Half of them did not apply the tools of social media marketing. The other half was involved in discussions on health-related online forums (moderation or consulting). Relatively few offices use social media and social media marketing in health promotion campaigns. The use of social media by the Marshal Offices may result in a potential increase in effectiveness of the pro-health campaigns. It is recommended that Polish Marshal Offices recognize the potential benefits of social media marketing campaign instruments in the field of health promotion in order to reach out the digital recipients.

Social inheritance can explain the structure of animal social networks

PubMed Central

Ilany, Amiyaal; Akçay, Erol

2016-01-01

The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals. But as of yet, no general theory of social network structure exists that can explain the diversity of social networks observed in nature, and serve as a null model for detecting species and population-specific factors. Here we propose a simple and generally applicable model of social network structure. We consider the emergence of network structure as a result of social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly. We compare model output with data from several species, showing that it can generate networks with properties such as those observed in real social systems. Our model demonstrates that important observed properties of social networks, including heritability of network position or assortative associations, can be understood as consequences of social inheritance. PMID:27352101

Unperturbed Schelling Segregation in Two or Three Dimensions

NASA Astrophysics Data System (ADS)

Barmpalias, George; Elwes, Richard; Lewis-Pye, Andrew

2016-09-01

Schelling's models of segregation, first described in 1969 (Am Econ Rev 59:488-493, 1969) are among the best known models of self-organising behaviour. Their original purpose was to identify mechanisms of urban racial segregation. But his models form part of a family which arises in statistical mechanics, neural networks, social science, and beyond, where populations of agents interact on networks. Despite extensive study, unperturbed Schelling models have largely resisted rigorous analysis, prior results generally focusing on variants in which noise is introduced into the dynamics, the resulting system being amenable to standard techniques from statistical mechanics or stochastic evolutionary game theory (Young in Individual strategy and social structure: an evolutionary theory of institutions, Princeton University Press, Princeton, 1998). A series of recent papers (Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012); Barmpalias et al. in: 55th annual IEEE symposium on foundations of computer science, Philadelphia, 2014, J Stat Phys 158:806-852, 2015), has seen the first rigorous analyses of 1-dimensional unperturbed Schelling models, in an asymptotic framework largely unknown in statistical mechanics. Here we provide the first such analysis of 2- and 3-dimensional unperturbed models, establishing most of the phase diagram, and answering a challenge from Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012).

Establishing the reliability of rhesus macaque social network assessment from video observations

PubMed Central

Feczko, Eric; Mitchell, Thomas A. J.; Walum, Hasse; Brooks, Jenna M.; Heitz, Thomas R.; Young, Larry J.; Parr, Lisa A.

2015-01-01

Understanding the properties of a social environment is important for understanding the dynamics of social relationships. Understanding such dynamics is relevant for multiple fields, ranging from animal behaviour to social and cognitive neuroscience. To quantify social environment properties, recent studies have incorporated social network analysis. Social network analysis quantifies both the global and local properties of a social environment, such as social network efficiency and the roles played by specific individuals, respectively. Despite the plethora of studies incorporating social network analysis, methods to determine the amount of data necessary to derive reliable social networks are still being developed. Determining the amount of data necessary for a reliable network is critical for measuring changes in the social environment, for example following an experimental manipulation, and therefore may be critical for using social network analysis to statistically assess social behaviour. In this paper, we extend methods for measuring error in acquired data and for determining the amount of data necessary to generate reliable social networks. We derived social networks from a group of 10 male rhesus macaques, Macaca mulatta, for three behaviours: spatial proximity, grooming and mounting. Behaviours were coded using a video observation technique, where video cameras recorded the compound where the 10 macaques resided. We collected, coded and used 10 h of video data to construct these networks. Using the methods described here, we found in our data that 1 h of spatial proximity observations produced reliable social networks. However, this may not be true for other studies due to differences in data acquisition. Our results have broad implications for measuring and predicting the amount of error in any social network, regardless of species. PMID:26392632

Dim Networks: The Utility of Social Network Analysis for Illuminating Partner Security Force Networks

DTIC Science & Technology

2015-12-01

use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations and selectively target key elements...data to improve SC. 14. SUBJECT TERMS social network analysis, dark networks, light networks, dim networks, security cooperation, Southeast Asia...task may already exist. Recently, the use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations

Objective measures, sensors and computational techniques for stress recognition and classification: a survey.

PubMed

Sharma, Nandita; Gedeon, Tom

2012-12-01

Stress is a major growing concern in our day and age adversely impacting both individuals and society. Stress research has a wide range of benefits from improving personal operations, learning, and increasing work productivity to benefiting society - making it an interesting and socially beneficial area of research. This survey reviews sensors that have been used to measure stress and investigates techniques for modelling stress. It discusses non-invasive and unobtrusive sensors for measuring computed stress, a term we coin in the paper. Sensors that do not impede everyday activities that could be used by those who would like to monitor stress levels on a regular basis (e.g. vehicle drivers, patients with illnesses linked to stress) is the focus of the discussion. Computational techniques have the capacity to determine optimal sensor fusion and automate data analysis for stress recognition and classification. Several computational techniques have been developed to model stress based on techniques such as Bayesian networks, artificial neural networks, and support vector machines, which this survey investigates. The survey concludes with a summary and provides possible directions for further computational stress research. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

[Social Networks of Children with Mentally Ill Parents].

PubMed

Stiawa, Maja; Kilian, Reinhold

2017-10-01

Social Networks of Children with Mentally Ill Parents Mental illness of parents can be a load situation for children. Supporting social relations might be an important source in such a situation. Social relations can be shown by social network analysis. Studies about social networks and mental health indicate differences regarding structure and potential for support when compared with social networks of healthy individuals. If and how mental illness of parents has an impact on their children's network is widely unknown. This systematic review shows methods and results of studies about social networks of children with mentally ill parents. By systematic search in electronic databases as well as manual search, two studies were found who met the target criteria. Both studies were conducted in the USA. Results of studies indicate that parental mental illness affects the state of mental health and social networks of children. Symptomatology of children changed due to perceived social support of network contacts. Impact of social support and strong network contacts seems to depend on age of children and the family situation. That's why support offers should be adapt to children's age. Focusing on social networks as potential resource for support and needs of the family affected seems appropriate during treatment.

From Social Integration to Social Isolation: The Relationship Between Social Network Types and Perceived Availability of Social Support in a National Sample of Older Canadians.

PubMed

Harasemiw, Oksana; Newall, Nancy; Shooshtari, Shahin; Mackenzie, Corey; Menec, Verena

2017-01-01

It is well-documented that social isolation is detrimental to health and well-being. What is less clear is what types of social networks allow older adults to get the social support they need to promote health and well-being. In this study, we identified social network types in a national sample of older Canadians and explored whether they are associated with perceived availability of different types of social support (affectionate, emotional, or tangible, and positive social interactions). Data were drawn from the baseline questionnaire of the Canadian Longitudinal Study on Aging for participants aged 65-85 (unweighted n = 8,782). Cluster analyses revealed six social network groups. Social support generally declined as social networks became more restricted; however, different patterns of social support availability emerged for different social network groups. These findings suggest that certain types of social networks place older adults at risk of not having met specific social support needs.

Functional connectivity associated with social networks in older adults: A resting-state fMRI study.

PubMed

Pillemer, Sarah; Holtzer, Roee; Blumen, Helena M

2017-06-01

Poor social networks and decreased levels of social support are associated with worse mood, health, and cognition in younger and older adults. Yet, we know very little about the brain substrates associated with social networks and social support, particularly in older adults. This study examined functional brain substrates associated with social networks using the Social Network Index (SNI) and resting-state functional magnetic resonance imaging (fMRI). Resting-state fMRI data from 28 non-demented older adults were analyzed with independent components analyses. As expected, four established resting-state networks-previously linked to motor, vision, speech, and other language functions-correlated with the quality (SNI-1: total number of high-contact roles of a respondent) and quantity (SNI-2: total number of individuals in a respondent's social network) of social networks: a sensorimotor, a visual, a vestibular/insular, and a left frontoparietal network. Moreover, SNI-1 was associated with greater functional connectivity in the lateral prefrontal regions of the left frontoparietal network, while SNI-2 was associated with greater functional connectivity in the medial prefrontal regions of this network. Thus, lateral prefrontal regions may be particularly linked to the quality of social networks while medial prefrontal regions may be particularly linked to the quantity of social networks.

Intra-organizational Computation and Complexity

DTIC Science & Technology

2003-01-01

models. New methodologies, centered on understanding algorithmic complexity, are being developed that may enable us to better handle network data ...tractability of data analysis, and enable more precise theorization. A variety of measures of algorithmic complexity, e.g., Kolmogorov-Chaitin, and a...variety of proxies exist (which are often turned to for pragmatic reasons) ( Lempel and Ziv ,1976). For the most part, social and organizational

Using FarmVille in an Introductory Managerial Accounting Course to Engage Students, Enhance Comprehension, and Develop Social Networking Skills

ERIC Educational Resources Information Center

Krom, Cynthia L.

2012-01-01

This article explores the use of the free Zynga computer game FarmVille, which is played in conjunction with Facebook[R], to facilitate active learning in a managerial accounting course. Results indicate that it invokes an improved understanding of the accounting content, particularly among nonaccounting majors; a high level of student…

A survey of big data research

PubMed Central

Fang, Hua; Zhang, Zhaoyang; Wang, Chanpaul Jin; Daneshmand, Mahmoud; Wang, Chonggang; Wang, Honggang

2015-01-01

Big data create values for business and research, but pose significant challenges in terms of networking, storage, management, analytics and ethics. Multidisciplinary collaborations from engineers, computer scientists, statisticians and social scientists are needed to tackle, discover and understand big data. This survey presents an overview of big data initiatives, technologies and research in industries and academia, and discusses challenges and potential solutions. PMID:26504265

Teach Ourselves: Social Networks for CS Stem Education

DTIC Science & Technology

2014-12-01

with peers. Teach Ourselves includes features that were inspired by recent research on the engaging properties of computer games , including the chance...15 i 1.0 SUMMARY In an online learning community (“ Teach Ourselves...content shared and viewed on the Internet, including text, images, videos and even home-authored games . The ease with which content can now be created

Learner Views about Cooperative Learning in Social Learning Networks

ERIC Educational Resources Information Center

Cankaya, Serkan; Yunkul, Eyup

2018-01-01

The purpose of this study was to reveal the attitudes and views of university students about the use of Edmodo as a cooperative learning environment. In the research process, the students were divided into groups of 4 or 5 within the scope of a course given in the department of Computer Education and Instructional Technology. For each group,…

What Are Young People Doing on Internet? Use of ICT, Parental Supervision Strategies and Exposure to Risks

ERIC Educational Resources Information Center

Giménez, Ana M.; Luengo, José A.; Bartrina, M. José

2017-01-01

Introduction: Current research emphasizes young people's access to and use of social networks, chat and WhatsApp. However, this situation is not associated with active parental mediation to protect them from the risks involved. This study analyzes Murcian students' perception of cell phone and computer use, parental mediation strategies and their…

Possible Effects of Electronic Social Media on Gifted and Talented Children's Intelligence and Emotional Development

ERIC Educational Resources Information Center

Freeman, Joan

2016-01-01

In just a few decades, electronic networks have expanded beyond all expectations. Instant messaging via smart phones and computers of all sorts whizz round the world. Researchers and policy makers are strongly divided in their findings and conclusions as to the effects of possible persuasive changes on the minds and lives of children, particularly…

Supporting Regularized Logistic Regression Privately and Efficiently.

PubMed

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.

Supporting Regularized Logistic Regression Privately and Efficiently

PubMed Central

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. PMID:27271738

QuakeSim Project Networking

NASA Astrophysics Data System (ADS)

Kong, D.; Donnellan, A.; Pierce, M. E.

2012-12-01

QuakeSim is an online computational framework focused on using remotely sensed geodetic imaging data to model and understand earthquakes. With the rise in online social networking over the last decade, many tools and concepts have been developed that are useful to research groups. In particular, QuakeSim is interested in the ability for researchers to post, share, and annotate files generated by modeling tools in order to facilitate collaboration. To accomplish this, features were added to the preexisting QuakeSim site that include single sign-on, automated saving of output from modeling tools, and a personal user space to manage sharing permissions on these saved files. These features implement OpenID and Lightweight Data Access Protocol (LDAP) technologies to manage files across several different servers, including a web server running Drupal and other servers hosting the computational tools themselves.

Understanding complex interactions using social network analysis.

PubMed

Pow, Janette; Gayen, Kaberi; Elliott, Lawrie; Raeside, Robert

2012-10-01

The aim of this paper is to raise the awareness of social network analysis as a method to facilitate research in nursing research. The application of social network analysis in assessing network properties has allowed greater insight to be gained in many areas including sociology, politics, business organisation and health care. However, the use of social networks in nursing has not received sufficient attention. Review of literature and illustration of the application of the method of social network analysis using research examples. First, the value of social networks will be discussed. Then by using illustrative examples, the value of social network analysis to nursing will be demonstrated. The method of social network analysis is found to give greater insights into social situations involving interactions between individuals and has particular application to the study of interactions between nurses and between nurses and patients and other actors. Social networks are systems in which people interact. Two quantitative techniques help our understanding of these networks. The first is visualisation of the network. The second is centrality. Individuals with high centrality are key communicators in a network. Applying social network analysis to nursing provides a simple method that helps gain an understanding of human interaction and how this might influence various health outcomes. It allows influential individuals (actors) to be identified. Their influence on the formation of social norms and communication can determine the extent to which new interventions or ways of thinking are accepted by a group. Thus, working with key individuals in a network could be critical to the success and sustainability of an intervention. Social network analysis can also help to assess the effectiveness of such interventions for the recipient and the service provider. © 2012 Blackwell Publishing Ltd.

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…

Parsing Social Network Survey Data from Hidden Populations Using Stochastic Context-Free Grammars

PubMed Central

Poon, Art F. Y.; Brouwer, Kimberly C.; Strathdee, Steffanie A.; Firestone-Cruz, Michelle; Lozada, Remedios M.; Kosakovsky Pond, Sergei L.; Heckathorn, Douglas D.; Frost, Simon D. W.

2009-01-01

Background Human populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a ÔhiddenÕ population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, e.g., respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical models rely on unrealistic assumptions, e.g., that the traversal of social networks can be modeled by a Markov chain rather than a branching process. Methodology/Principal Findings Here, we develop a new methodology based on stochastic context-free grammars (SCFGs), which are well-suited to modeling tree-like structure of the RDS recruitment process. We apply this methodology to an RDS case study of injection drug users (IDUs) in Tijuana, México, a hidden population at high risk of blood-borne and sexually-transmitted infections (i.e., HIV, hepatitis C virus, syphilis). Survey data were encoded as text strings that were parsed using our custom implementation of the inside-outside algorithm in a publicly-available software package (HyPhy), which uses either expectation maximization or direct optimization methods and permits constraints on model parameters for hypothesis testing. We identified significant latent variability in the recruitment process that violates assumptions of Markov chain-based methods for RDS analysis: firstly, IDUs tended to emulate the recruitment behavior of their own recruiter; and secondly, the recruitment of like peers (homophily) was dependent on the number of recruits. Conclusions SCFGs provide a rich probabilistic language that can articulate complex latent structure in survey data derived from the traversal of social networks. Such structure that has no representation in Markov chain-based models can interfere with the estimation of the composition of hidden populations if left unaccounted for, raising critical implications for the prevention and control of infectious disease epidemics. PMID:19738904

Social networks of patients with psychosis: a systematic review.

PubMed

Palumbo, Claudia; Volpe, Umberto; Matanov, Aleksandra; Priebe, Stefan; Giacco, Domenico

2015-10-12

Social networks are important for mental health outcomes as they can mobilise resources and help individuals to cope with social stressors. Individuals with psychosis may have specific difficulties in establishing and maintaining social relationships which impacts on their well-being and quality of life. There has been a growing interest in developing social network interventions for patients with psychotic disorders. A systematic literature review was conducted to investigate the size of social networks of patients with psychotic disorders, as well as their friendship networks. A systematic electronic search was carried out in MEDLINE, EMBASE and PsychINFO databases using a combination of search terms relating to 'social network', 'friendship' and 'psychotic disorder'. The search identified 23 relevant papers. Out of them, 20 reported patient social network size. Four papers reported the mean number of friends in addition to whole network size, while three further papers focused exclusively on the number of friends. Findings varied substantially across the studies, with a weighted mean size of 11.7 individuals for whole social networks and 3.4 individuals for friendship networks. On average, 43.1 % of the whole social network was composed of family members, while friends accounted for 26.5 %. Studies assessing whole social network size and friendship networks of people with psychosis are difficult to compare as different concepts and methods of assessment were applied. The extent of the overlap between different social roles assessed in the networks was not always clear. Greater conceptual and methodological clarity is needed in order to help the development of effective strategies to increase social resources of patients with psychosis.

Sampling Based Influence Maximization on Linear Threshold Model

NASA Astrophysics Data System (ADS)

Jia, Su; Chen, Ling

2018-04-01

A sampling based influence maximization on linear threshold (LT) model method is presented. The method samples the routes in the possible worlds in the social networks, and uses Chernoff bound to estimate the number of samples so that the error can be constrained within a given bound. Then the active possibilities of the routes in the possible worlds are calculated, and are used to compute the influence spread of each node in the network. Our experimental results show that our method can effectively select appropriate seed nodes set that spreads larger influence than other similar methods.

Relationship between Social Networks Adoption and Social Intelligence

ERIC Educational Resources Information Center

Gunduz, Semseddin

2017-01-01

The purpose of this study was to set forth the relationship between the individuals' states to adopt social networks and social intelligence and analyze both concepts according to various variables. Research data were collected from 1145 social network users in the online media by using the Adoption of Social Network Scale and Social Intelligence…