Cooperative behavior and phase transitions in co-evolving stag hunt game

NASA Astrophysics Data System (ADS)

Zhang, W.; Li, Y. S.; Xu, C.; Hui, P. M.

2016-02-01

Cooperative behavior and different phases in a co-evolving network dynamics based on the stag hunt game is studied. The dynamical processes are parameterized by a payoff r that tends to promote non-cooperative behavior and a probability q for a rewiring attempt that could isolate the non-cooperators. The interplay between the parameters leads to different phases. Detailed simulations and a mean field theory are employed to reveal the properties of different phases. For small r, the cooperators are the majority and form a connected cluster while the non-cooperators increase with q but remain isolated over the whole range of q, and it is a static phase. For sufficiently large r, cooperators disappear in an intermediate range qL ≤ q ≤qU and a dynamical all-non-cooperators phase results. For q >qU, a static phase results again. A mean field theory based on how the link densities change in time by the co-evolving dynamics is constructed. The theory gives a phase diagram in the q- r parameter space that is qualitatively in agreement with simulation results. The sources of discrepancies between theory and simulations are discussed.

Graph fission in an evolving voter model

PubMed Central

Durrett, Richard; Gleeson, James P.; Lloyd, Alun L.; Mucha, Peter J.; Shi, Feng; Sivakoff, David; Socolar, Joshua E. S.; Varghese, Chris

2012-01-01

We consider a simplified model of a social network in which individuals have one of two opinions (called 0 and 1) and their opinions and the network connections coevolve. Edges are picked at random. If the two connected individuals hold different opinions then, with probability 1 - α, one imitates the opinion of the other; otherwise (i.e., with probability α), the link between them is broken and one of them makes a new connection to an individual chosen at random (i) from those with the same opinion or (ii) from the network as a whole. The evolution of the system stops when there are no longer any discordant edges connecting individuals with different opinions. Letting ρ be the fraction of voters holding the minority opinion after the evolution stops, we are interested in how ρ depends on α and the initial fraction u of voters with opinion 1. In case (i), there is a critical value αc which does not depend on u, with ρ ≈ u for α > αc and ρ ≈ 0 for α < αc. In case (ii), the transition point αc(u) depends on the initial density u. For α > αc(u), ρ ≈ u, but for α < αc(u), we have ρ(α,u) = ρ(α,1/2). Using simulations and approximate calculations, we explain why these two nearly identical models have such dramatically different phase transitions. PMID:22355142

Multiplex PageRank.

PubMed

Halu, Arda; Mondragón, Raúl J; Panzarasa, Pietro; Bianconi, Ginestra

2013-01-01

Many complex systems can be described as multiplex networks in which the same nodes can interact with one another in different layers, thus forming a set of interacting and co-evolving networks. Examples of such multiplex systems are social networks where people are involved in different types of relationships and interact through various forms of communication media. The ranking of nodes in multiplex networks is one of the most pressing and challenging tasks that research on complex networks is currently facing. When pairs of nodes can be connected through multiple links and in multiple layers, the ranking of nodes should necessarily reflect the importance of nodes in one layer as well as their importance in other interdependent layers. In this paper, we draw on the idea of biased random walks to define the Multiplex PageRank centrality measure in which the effects of the interplay between networks on the centrality of nodes are directly taken into account. In particular, depending on the intensity of the interaction between layers, we define the Additive, Multiplicative, Combined, and Neutral versions of Multiplex PageRank, and show how each version reflects the extent to which the importance of a node in one layer affects the importance the node can gain in another layer. We discuss these measures and apply them to an online multiplex social network. Findings indicate that taking the multiplex nature of the network into account helps uncover the emergence of rankings of nodes that differ from the rankings obtained from one single layer. Results provide support in favor of the salience of multiplex centrality measures, like Multiplex PageRank, for assessing the prominence of nodes embedded in multiple interacting networks, and for shedding a new light on structural properties that would otherwise remain undetected if each of the interacting networks were analyzed in isolation.

Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins

NASA Astrophysics Data System (ADS)

Champeimont, Raphaël; Laine, Elodie; Hu, Shuang-Wei; Penin, Francois; Carbone, Alessandra

2016-05-01

A novel computational approach of coevolution analysis allowed us to reconstruct the protein-protein interaction network of the Hepatitis C Virus (HCV) at the residue resolution. For the first time, coevolution analysis of an entire viral genome was realized, based on a limited set of protein sequences with high sequence identity within genotypes. The identified coevolving residues constitute highly relevant predictions of protein-protein interactions for further experimental identification of HCV protein complexes. The method can be used to analyse other viral genomes and to predict the associated protein interaction networks.

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

PubMed Central

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

2014-01-01

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

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

PubMed

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

2014-05-06

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

Coevolving residues of (beta/alpha)(8)-barrel proteins play roles in stabilizing active site architecture and coordinating protein dynamics.

PubMed

Shen, Hongbo; Xu, Feng; Hu, Hairong; Wang, Feifei; Wu, Qi; Huang, Qiang; Wang, Honghai

2008-12-01

Indole-3-glycerol phosphate synthase (IGPS) is a representative of (beta/alpha)(8)-barrel proteins-the most common enzyme fold in nature. To better understand how the constituent amino-acids work together to define the structure and to facilitate the function, we investigated the evolutionary and dynamical coupling of IGPS residues by combining statistical coupling analysis (SCA) and molecular dynamics (MD) simulations. The coevolving residues identified by the SCA were found to form a network which encloses the active site completely. The MD simulations showed that these coevolving residues are involved in the correlated and anti-correlated motions. The correlated residues are within van der Waals contact and appear to maintain the active site architecture; the anti-correlated residues are mainly distributed on opposite sides of the catalytic cavity and coordinate the motions likely required for the substrate entry and product release. Our findings might have broad implications for proteins with the highly conserved (betaalpha)(8)-barrel in assessing the roles of amino-acids that are moderately conserved and not directly involved in the active site of the (beta/alpha)(8)-barrel. The results of this study could also provide useful information for further exploring the specific residue motions for the catalysis and protein design based on the (beta/alpha)(8)-barrel scaffold.

Functional Inference of Complex Anatomical Tendinous Networks at a Macroscopic Scale via Sparse Experimentation

PubMed Central

Saxena, Anupam; Lipson, Hod; Valero-Cuevas, Francisco J.

2012-01-01

In systems and computational biology, much effort is devoted to functional identification of systems and networks at the molecular-or cellular scale. However, similarly important networks exist at anatomical scales such as the tendon network of human fingers: the complex array of collagen fibers that transmits and distributes muscle forces to finger joints. This network is critical to the versatility of the human hand, and its function has been debated since at least the 16th century. Here, we experimentally infer the structure (both topology and parameter values) of this network through sparse interrogation with force inputs. A population of models representing this structure co-evolves in simulation with a population of informative future force inputs via the predator-prey estimation-exploration algorithm. Model fitness depends on their ability to explain experimental data, while the fitness of future force inputs depends on causing maximal functional discrepancy among current models. We validate our approach by inferring two known synthetic Latex networks, and one anatomical tendon network harvested from a cadaver's middle finger. We find that functionally similar but structurally diverse models can exist within a narrow range of the training set and cross-validation errors. For the Latex networks, models with low training set error [<4%] and resembling the known network have the smallest cross-validation errors [∼5%]. The low training set [<4%] and cross validation [<7.2%] errors for models for the cadaveric specimen demonstrate what, to our knowledge, is the first experimental inference of the functional structure of complex anatomical networks. This work expands current bioinformatics inference approaches by demonstrating that sparse, yet informative interrogation of biological specimens holds significant computational advantages in accurate and efficient inference over random testing, or assuming model topology and only inferring parameters values. These findings also hold clues to both our evolutionary history and the development of versatile machines. PMID:23144601

Functional inference of complex anatomical tendinous networks at a macroscopic scale via sparse experimentation.

PubMed

Saxena, Anupam; Lipson, Hod; Valero-Cuevas, Francisco J

2012-01-01

In systems and computational biology, much effort is devoted to functional identification of systems and networks at the molecular-or cellular scale. However, similarly important networks exist at anatomical scales such as the tendon network of human fingers: the complex array of collagen fibers that transmits and distributes muscle forces to finger joints. This network is critical to the versatility of the human hand, and its function has been debated since at least the 16(th) century. Here, we experimentally infer the structure (both topology and parameter values) of this network through sparse interrogation with force inputs. A population of models representing this structure co-evolves in simulation with a population of informative future force inputs via the predator-prey estimation-exploration algorithm. Model fitness depends on their ability to explain experimental data, while the fitness of future force inputs depends on causing maximal functional discrepancy among current models. We validate our approach by inferring two known synthetic Latex networks, and one anatomical tendon network harvested from a cadaver's middle finger. We find that functionally similar but structurally diverse models can exist within a narrow range of the training set and cross-validation errors. For the Latex networks, models with low training set error [<4%] and resembling the known network have the smallest cross-validation errors [∼5%]. The low training set [<4%] and cross validation [<7.2%] errors for models for the cadaveric specimen demonstrate what, to our knowledge, is the first experimental inference of the functional structure of complex anatomical networks. This work expands current bioinformatics inference approaches by demonstrating that sparse, yet informative interrogation of biological specimens holds significant computational advantages in accurate and efficient inference over random testing, or assuming model topology and only inferring parameters values. These findings also hold clues to both our evolutionary history and the development of versatile machines.

Partner choice cooperation in prisoner's dilemma

NASA Astrophysics Data System (ADS)

Wang, Qi; Xu, Zhaojin; Zhang, Lianzhong

2017-12-01

In this paper, we investigated the cooperative behavior in prisoner's dilemma when the individual behaviors and interaction structures could coevolve. Here, we study the model that the individuals can imitate the strategy of their neighbors and rewire their social ties throughout evolution, based exclusively on a fitness comparison. We find that the cooperation can be achieved if the time scale of network adaptation is large enough, even when the social dilemma strength is very strong. Detailed investigation shows that the presence or absence of the network adaptation has a profound impact on the collective behavior in the system.

Conflict and convention in dynamic networks.

PubMed

Foley, Michael; Forber, Patrick; Smead, Rory; Riedl, Christoph

2018-03-01

An important way to resolve games of conflict (snowdrift, hawk-dove, chicken) involves adopting a convention: a correlated equilibrium that avoids any conflict between aggressive strategies. Dynamic networks allow individuals to resolve conflict via their network connections rather than changing their strategy. Exploring how behavioural strategies coevolve with social networks reveals new dynamics that can help explain the origins and robustness of conventions. Here, we model the emergence of conventions as correlated equilibria in dynamic networks. Our results show that networks have the tendency to break the symmetry between the two conventional solutions in a strongly biased way. Rather than the correlated equilibrium associated with ownership norms (play aggressive at home, not away), we usually see the opposite host-guest norm (play aggressive away, not at home) evolve on dynamic networks, a phenomenon common to human interaction. We also show that learning to avoid conflict can produce realistic network structures in a way different than preferential attachment models. © 2017 The Author(s).

Diversity in the origins of proteostasis networks- a driver for protein function in evolution

PubMed Central

Powers, Evan T.; Balch, William E.

2013-01-01

Although a protein’s primary sequence largely determines its function, proteins can adopt different folding states in response to changes in the environment, some of which may be deleterious to the organism. All organisms, including Bacteria, Archaea and Eukarya, have evolved a protein homeostasis network, or proteostasis network, that consists of chaperones and folding factors, degradation components, signalling pathways and specialized compartmentalized modules that manage protein folding in response to environmental stimuli and variation. Surveying the origins of proteostasis networks reveals that they have co-evolved with the proteome to regulate the physiological state of the cell, reflecting the unique stresses that different cells or organisms experience, and that they have a key role in driving evolution by closely managing the link between the phenotype and the genotype. PMID:23463216

Designing a network of critical zone observatories to explore the living skin of the terrestrial Earth

Treesearch

Susan L. Brantley; William H. McDowell; William E. Dietrich; Timothy S. White; Praveen Kumar; Suzanne P. Anderson; Jon Chorover; Kathleen Ann Lohse; Roger C. Bales; Daniel D. Richter; Gordon Grant; Jérôme Gaillardet

2017-01-01

The critical zone (CZ), the dynamic living skin of the Earth, extends from the top of the vegetative canopy through the soil and down to fresh bedrock and the bottom of the groundwater. All humans live in and depend on the CZ. This zone has three co-evolving surfaces: the top of the vegetative canopy, the ground surface, and a deep subsurface below which Earth’s...

Linking Individual and Collective Behavior in Adaptive Social Networks

NASA Astrophysics Data System (ADS)

Pinheiro, Flávio L.; Santos, Francisco C.; Pacheco, Jorge M.

2016-03-01

Adaptive social structures are known to promote the evolution of cooperation. However, up to now the characterization of the collective, population-wide dynamics resulting from the self-organization of individual strategies on a coevolving, adaptive network has remained unfeasible. Here we establish a (reversible) link between individual (micro)behavior and collective (macro)behavior for coevolutionary processes. We demonstrate that an adaptive network transforms a two-person social dilemma locally faced by individuals into a collective dynamics that resembles that associated with an N -person coordination game, whose characterization depends sensitively on the relative time scales between the entangled behavioral and network evolutions. In particular, we show that the faster the relative rate of adaptation of the network, the smaller the critical fraction of cooperators required for cooperation to prevail, thus establishing a direct link between network adaptation and the evolution of cooperation. The framework developed here is general and may be readily applied to other dynamical processes occurring on adaptive networks, notably, the spreading of contagious diseases or the diffusion of innovations.

Modeling complexity in engineered infrastructure system: Water distribution network as an example

NASA Astrophysics Data System (ADS)

Zeng, Fang; Li, Xiang; Li, Ke

2017-02-01

The complex topology and adaptive behavior of infrastructure systems are driven by both self-organization of the demand and rigid engineering solutions. Therefore, engineering complex systems requires a method balancing holism and reductionism. To model the growth of water distribution networks, a complex network model was developed following the combination of local optimization rules and engineering considerations. The demand node generation is dynamic and follows the scaling law of urban growth. The proposed model can generate a water distribution network (WDN) similar to reported real-world WDNs on some structural properties. Comparison with different modeling approaches indicates that a realistic demand node distribution and co-evolvement of demand node and network are important for the simulation of real complex networks. The simulation results indicate that the efficiency of water distribution networks is exponentially affected by the urban growth pattern. On the contrary, the improvement of efficiency by engineering optimization is limited and relatively insignificant. The redundancy and robustness, on another aspect, can be significantly improved through engineering methods.

Co-evolving prisoner's dilemma: Performance indicators and analytic approaches

NASA Astrophysics Data System (ADS)

Zhang, W.; Choi, C. W.; Li, Y. S.; Xu, C.; Hui, P. M.

2017-02-01

Understanding the intrinsic relation between the dynamical processes in a co-evolving network and the necessary ingredients in formulating a reliable theory is an important question and a challenging task. Using two slightly different definitions of performance indicator in the context of a co-evolving prisoner's dilemma game, it is shown that very different cooperative levels result and theories of different complexity are required to understand the key features. When the payoff per opponent is used as the indicator (Case A), non-cooperative strategy has an edge and dominates in a large part of the parameter space formed by the cutting-and-rewiring probability and the strategy imitation probability. When the payoff from all opponents is used (Case B), cooperative strategy has an edge and dominates the parameter space. Two distinct phases, one homogeneous and dynamical and another inhomogeneous and static, emerge and the phase boundary in the parameter space is studied in detail. A simple theory assuming an average competing environment for cooperative agents and another for non-cooperative agents is shown to perform well in Case A. The same theory, however, fails badly for Case B. It is necessary to include more spatial correlation into a theory for Case B. We show that the local configuration approximation, which takes into account of the different competing environments for agents with different strategies and degrees, is needed to give reliable results for Case B. The results illustrate that formulating a proper theory requires both a conceptual understanding of the effects of the adaptive processes in the problem and a delicate balance between simplicity and accuracy.

Multi-strategy coevolving aging particle optimization.

PubMed

Iacca, Giovanni; Caraffini, Fabio; Neri, Ferrante

2014-02-01

We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer.

Truth and probability in evolutionary games

NASA Astrophysics Data System (ADS)

Barrett, Jeffrey A.

2017-01-01

This paper concerns two composite Lewis-Skyrms signalling games. Each consists in a base game that evolves a language descriptive of nature and a metagame that coevolves a language descriptive of the base game and its evolving language. The first composite game shows how a pragmatic notion of truth might coevolve with a simple descriptive language. The second shows how a pragmatic notion of probability might similarly coevolve. Each of these pragmatic notions is characterised by the particular game and role that it comes to play in the game.

Hotspots for allosteric regulation on protein surfaces

PubMed Central

Reynolds, Kimberly A.; McLaughlin, Richard N.; Ranganathan, Rama

2012-01-01

Recent work indicates a general architecture for proteins in which sparse networks of physically contiguous and co-evolving amino acids underlie basic aspects of structure and function. These networks, termed sectors, are spatially organized such that active sites are linked to many surface sites distributed throughout the structure. Using the metabolic enzyme dihydrofolate reductase as a model system, we show that (1) the sector is strongly correlated to a network of residues undergoing millisecond conformational fluctuations associated with enzyme catalysis and (2) sector-connected surface sites are statistically preferred locations for the emergence of allosteric control in vivo. Thus, sectors represent an evolutionarily conserved “wiring” mechanism that can enable perturbations at specific surface positions to rapidly initiate conformational control over protein function. These findings suggest that sectors enable the evolution of intermolecular communication and regulation. PMID:22196731

Macroscopic description of complex adaptive networks coevolving with dynamic node states

NASA Astrophysics Data System (ADS)

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

2015-05-01

In many real-world complex systems, the time evolution of the network's structure and the dynamic state of its nodes are closely entangled. Here we study opinion formation and imitation on an adaptive complex network which is dependent on the individual dynamic state of each node and vice versa to model the coevolution of renewable resources with the dynamics of harvesting agents on a social network. The adaptive voter model is coupled to a set of identical logistic growth models and we mainly find that, in such systems, the rate of interactions between nodes as well as the adaptive rewiring probability are crucial parameters for controlling the sustainability of the system's equilibrium state. We derive a macroscopic description of the system in terms of ordinary differential equations which provides a general framework to model and quantify the influence of single node dynamics on the macroscopic state of the network. The thus obtained framework is applicable to many fields of study, such as epidemic spreading, opinion formation, or socioecological modeling.

The prisoner’s dilemma on co-evolving networks under perfect rationality

NASA Astrophysics Data System (ADS)

Biely, Christoly; Dragosits, Klaus; Thurner, Stefan

2007-04-01

We consider the prisoner’s dilemma being played repeatedly on a dynamic network, where agents may choose their actions as well as their co-players. This leads to co-evolution of network structure and strategy patterns of the players. Individual decisions are made fully rationally and are based on local information only. They are made such that links to defecting agents are resolved and that cooperating agents build up new links. The exact form of the updating scheme is motivated by profit maximization and not by imitation. If players update their decisions in a synchronized way the system exhibits oscillatory dynamics: Periods of growing cooperation (and total linkage) alternate with periods of increasing defection. The cyclical behavior is reduced and the system stabilizes at significant total cooperation levels when players are less synchronized. In this regime we find emergent network structures resembling ‘complex’ and hierarchical topology. The exponent of the power-law degree distribution ( γ∼8.6) perfectly matches empirical results for human communication networks.

Macroscopic description of complex adaptive networks coevolving with dynamic node states.

PubMed

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

2015-05-01

In many real-world complex systems, the time evolution of the network's structure and the dynamic state of its nodes are closely entangled. Here we study opinion formation and imitation on an adaptive complex network which is dependent on the individual dynamic state of each node and vice versa to model the coevolution of renewable resources with the dynamics of harvesting agents on a social network. The adaptive voter model is coupled to a set of identical logistic growth models and we mainly find that, in such systems, the rate of interactions between nodes as well as the adaptive rewiring probability are crucial parameters for controlling the sustainability of the system's equilibrium state. We derive a macroscopic description of the system in terms of ordinary differential equations which provides a general framework to model and quantify the influence of single node dynamics on the macroscopic state of the network. The thus obtained framework is applicable to many fields of study, such as epidemic spreading, opinion formation, or socioecological modeling.

Emergence of structural patterns out of synchronization in networks with competitive interactions

NASA Astrophysics Data System (ADS)

Assenza, Salvatore; Gutiérrez, Ricardo; Gómez-Gardeñes, Jesús; Latora, Vito; Boccaletti, Stefano

2011-09-01

Synchronization is a collective phenomenon occurring in systems of interacting units, and is ubiquitous in nature, society and technology. Recent studies have enlightened the important role played by the interaction topology on the emergence of synchronized states. However, most of these studies neglect that real world systems change their interaction patterns in time. Here, we analyze synchronization features in networks in which structural and dynamical features co-evolve. The feedback of the node dynamics on the interaction pattern is ruled by the competition of two mechanisms: homophily (reinforcing those interactions with other correlated units in the graph) and homeostasis (preserving the value of the input strength received by each unit). The competition between these two adaptive principles leads to the emergence of key structural properties observed in real world networks, such as modular and scale-free structures, together with a striking enhancement of local synchronization in systems with no global order.

Restrictions on biological adaptation in language evolution.

PubMed

Chater, Nick; Reali, Florencia; Christiansen, Morten H

2009-01-27

Language acquisition and processing are governed by genetic constraints. A crucial unresolved question is how far these genetic constraints have coevolved with language, perhaps resulting in a highly specialized and species-specific language "module," and how much language acquisition and processing redeploy preexisting cognitive machinery. In the present work, we explored the circumstances under which genes encoding language-specific properties could have coevolved with language itself. We present a theoretical model, implemented in computer simulations, of key aspects of the interaction of genes and language. Our results show that genes for language could have coevolved only with highly stable aspects of the linguistic environment; a rapidly changing linguistic environment does not provide a stable target for natural selection. Thus, a biological endowment could not coevolve with properties of language that began as learned cultural conventions, because cultural conventions change much more rapidly than genes. We argue that this rules out the possibility that arbitrary properties of language, including abstract syntactic principles governing phrase structure, case marking, and agreement, have been built into a "language module" by natural selection. The genetic basis of human language acquisition and processing did not coevolve with language, but primarily predates the emergence of language. As suggested by Darwin, the fit between language and its underlying mechanisms arose because language has evolved to fit the human brain, rather than the reverse.

Sustainable value creation through new industrial supply chains in apparel and fashion

NASA Astrophysics Data System (ADS)

Pal, R.; Sandberg, E.

2017-10-01

This paper explores the inter-organizational value creation, in apparel supply chain context, through circularity and digitalization for sustainability, by gathering evidences from vivid research experiences. It can be highlighted that inter-organizational value creation in both circular- and digital- apparel supply chains largely builds upon a variety of collaborative initiatives, and among a range of included members. Knowledge co-evolvement and business co-development, end-to-end integration and information transfer, and open networks are crucial to such collaborations - making development of new supply chain structures a meta-capability of apparel firms in the changing industrial landscape.

Accelerating consensus on coevolving networks: The effect of committed individuals

NASA Astrophysics Data System (ADS)

Singh, P.; Sreenivasan, S.; Szymanski, B. K.; Korniss, G.

2012-04-01

Social networks are not static but, rather, constantly evolve in time. One of the elements thought to drive the evolution of social network structure is homophily—the need for individuals to connect with others who are similar to them. In this paper, we study how the spread of a new opinion, idea, or behavior on such a homophily-driven social network is affected by the changing network structure. In particular, using simulations, we study a variant of the Axelrod model on a network with a homophily-driven rewiring rule imposed. First, we find that the presence of rewiring within the network, in general, impedes the reaching of consensus in opinion, as the time to reach consensus diverges exponentially with network size N. We then investigate whether the introduction of committed individuals who are rigid in their opinion on a particular issue can speed up the convergence to consensus on that issue. We demonstrate that as committed agents are added, beyond a critical value of the committed fraction, the consensus time growth becomes logarithmic in network size N. Furthermore, we show that slight changes in the interaction rule can produce strikingly different results in the scaling behavior of consensus time, Tc. However, the benefit gained by introducing committed agents is qualitatively preserved across all the interaction rules we consider.

Diversity Driven Coexistence: Collective Stability in the Cyclic Competition of Three Species

NASA Astrophysics Data System (ADS)

Bassler, Kevin E.; Frey, Erwin; Zia, R. K. P.

2015-03-01

The basic physics of collective behavior are often difficult to quantify and understand, particularly when the system is driven out of equilibrium. Many complex systems are usefully described as complex networks, consisting of nodes and links. The nodes specify individual components of the system and the links describe their interactions. When both nodes and links change dynamically, or `co-evolve', as happens in many realistic systems, complex mathematical structures are encountered, posing challenges to our understanding. In this context, we introduce a minimal system of node and link degrees of freedom, co-evolving with stochastic rules. Specifically, we show that diversity of social temperament (intro- or extroversion) can produce collective stable coexistence when three species compete cyclically. It is well-known that when only extroverts exist in a stochastic rock-paper-scissors game, or in a conserved predator-prey, Lotka-Volterra system, extinction occurs at times of O(N), where N is the number of nodes. We find that when both introverts and extroverts exist, where introverts sever social interactions and extroverts create them, collective coexistence prevails in long-living, quasi-stationary states. Work supported by the NSF through Grants DMR-1206839 (KEB) and DMR-1244666 (RKPZ), and by the AFOSR and DARPA through Grant FA9550-12-1-0405 (KEB).

Surface Sites for Engineering Allosteric Control in Proteins

PubMed Central

Lee, Jeeyeon; Natarajan, Madhusudan; Nashine, Vishal C.; Socolich, Michael; Vo, Tina; Russ, William P.; Benkovic, Stephen J.; Ranganathan, Rama

2010-01-01

Statistical analyses of protein families reveal networks of coevolving amino acids that functionally link distantly positioned functional surfaces. Such linkages suggest a concept for engineering allosteric control into proteins: The intramolecular networks of two proteins could be joined across their surface sites such that the activity of one protein might control the activity of the other. We tested this idea by creating PAS-DHFR, a designed chimeric protein that connects a light-sensing signaling domain from a plant member of the Per/Arnt/Sim (PAS) family of proteins with Escherichia coli dihydrofolate reductase (DHFR). With no optimization, PAS-DHFR exhibited light-dependent catalytic activity that depended on the site of connection and on known signaling mechanisms in both proteins. PAS-DHFR serves as a proof of concept for engineering regulatory activities into proteins through interface design at conserved allosteric sites. PMID:18927392

Origins of coevolution between residues distant in protein 3D structures

PubMed Central

Ovchinnikov, Sergey; Kamisetty, Hetunandan; Baker, David

2017-01-01

Residue pairs that directly coevolve in protein families are generally close in protein 3D structures. Here we study the exceptions to this general trend—directly coevolving residue pairs that are distant in protein structures—to determine the origins of evolutionary pressure on spatially distant residues and to understand the sources of error in contact-based structure prediction. Over a set of 4,000 protein families, we find that 25% of directly coevolving residue pairs are separated by more than 5 Å in protein structures and 3% by more than 15 Å. The majority (91%) of directly coevolving residue pairs in the 5–15 Å range are found to be in contact in at least one homologous structure—these exceptions arise from structural variation in the family in the region containing the residues. Thirty-five percent of the exceptions greater than 15 Å are at homo-oligomeric interfaces, 19% arise from family structural variation, and 27% are in repeat proteins likely reflecting alignment errors. Of the remaining long-range exceptions (<1% of the total number of coupled pairs), many can be attributed to close interactions in an oligomeric state. Overall, the results suggest that directly coevolving residue pairs not in repeat proteins are spatially proximal in at least one biologically relevant protein conformation within the family; we find little evidence for direct coupling between residues at spatially separated allosteric and functional sites or for increased direct coupling between residue pairs on putative allosteric pathways connecting them. PMID:28784799

Opinion diversity and community formation in adaptive networks

NASA Astrophysics Data System (ADS)

Yu, Y.; Xiao, G.; Li, G.; Tay, W. P.; Teoh, H. F.

2017-10-01

It is interesting and of significant importance to investigate how network structures co-evolve with opinions. In this article, we show that, a simple model integrating consensus formation, link rewiring, and opinion change allows complex system dynamics to emerge, driving the system into a dynamic equilibrium with the co-existence of diversified opinions. Specifically, similar opinion holders may form into communities yet with no strict community consensus; and rather than being separated into disconnected communities, different communities are connected by a non-trivial proportion of inter-community links. More importantly, we show that the complex dynamics may lead to different numbers of communities at the steady state with a given tolerance between different opinion holders. We construct a framework for theoretically analyzing the co-evolution process. Theoretical analysis and extensive simulation results reveal some useful insights into the complex co-evolution process, including the formation of dynamic equilibrium, the transition between different steady states with different numbers of communities, and the dynamics between opinion distribution and network modularity.

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.

Models for the modern power grid

NASA Astrophysics Data System (ADS)

Nardelli, Pedro H. J.; Rubido, Nicolas; Wang, Chengwei; Baptista, Murilo S.; Pomalaza-Raez, Carlos; Cardieri, Paulo; Latva-aho, Matti

2014-10-01

This article reviews different kinds of models for the electric power grid that can be used to understand the modern power system, the smart grid. From the physical network to abstract energy markets, we identify in the literature different aspects that co-determine the spatio-temporal multilayer dynamics of power system. We start our review by showing how the generation, transmission and distribution characteristics of the traditional power grids are already subject to complex behaviour appearing as a result of the the interplay between dynamics of the nodes and topology, namely synchronisation and cascade effects. When dealing with smart grids, the system complexity increases even more: on top of the physical network of power lines and controllable sources of electricity, the modernisation brings information networks, renewable intermittent generation, market liberalisation, prosumers, among other aspects. In this case, we forecast a dynamical co-evolution of the smart grid and other kind of networked systems that cannot be understood isolated. This review compiles recent results that model electric power grids as complex systems, going beyond pure technological aspects. From this perspective, we then indicate possible ways to incorporate the diverse co-evolving systems into the smart grid model using, for example, network theory and multi-agent simulation.

Coevolution of Cooperation and Partner Rewiring Range in Spatial Social Networks

NASA Astrophysics Data System (ADS)

Khoo, Tommy; Fu, Feng; Pauls, Scott

2016-11-01

In recent years, there has been growing interest in the study of coevolutionary games on networks. Despite much progress, little attention has been paid to spatially embedded networks, where the underlying geographic distance, rather than the graph distance, is an important and relevant aspect of the partner rewiring process. It thus remains largely unclear how individual partner rewiring range preference, local vs. global, emerges and affects cooperation. Here we explicitly address this issue using a coevolutionary model of cooperation and partner rewiring range preference in spatially embedded social networks. In contrast to local rewiring, global rewiring has no distance restriction but incurs a one-time cost upon establishing any long range link. We find that under a wide range of model parameters, global partner switching preference can coevolve with cooperation. Moreover, the resulting partner network is highly degree-heterogeneous with small average shortest path length while maintaining high clustering, thereby possessing small-world properties. We also discover an optimum availability of reputation information for the emergence of global cooperators, who form distant partnerships at a cost to themselves. From the coevolutionary perspective, our work may help explain the ubiquity of small-world topologies arising alongside cooperation in the real world.

Transforming growth factor β: a master regulator of the gut microbiota and immune cell interactions.

PubMed

Bauché, David; Marie, Julien C

2017-04-01

The relationship between host organisms and their microbiota has co-evolved towards an inter-dependent network of mutualistic interactions. This interplay is particularly well studied in the gastrointestinal tract, where microbiota and host immune cells can modulate each other directly, as well as indirectly, through the production and release of chemical molecules and signals. In this review, we define the functional impact of transforming growth factor-beta (TGF-β) on this complex interplay, especially through its modulation of the activity of local regulatory T cells (Tregs), type 17 helper (Th17) cells, innate lymphoid cells (ILCs) and B cells.

Cyber Security Research Frameworks For Coevolutionary Network Defense

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

Rush, George D.; Tauritz, Daniel Remy

Several architectures have been created for developing and testing systems used in network security, but most are meant to provide a platform for running cyber security experiments as opposed to automating experiment processes. In the first paper, we propose a framework termed Distributed Cyber Security Automation Framework for Experiments (DCAFE) that enables experiment automation and control in a distributed environment. Predictive analysis of adversaries is another thorny issue in cyber security. Game theory can be used to mathematically analyze adversary models, but its scalability limitations restrict its use. Computational game theory allows us to scale classical game theory to larger,more » more complex systems. In the second paper, we propose a framework termed Coevolutionary Agent-based Network Defense Lightweight Event System (CANDLES) that can coevolve attacker and defender agent strategies and capabilities and evaluate potential solutions with a custom network defense simulation. The third paper is a continuation of the CANDLES project in which we rewrote key parts of the framework. Attackers and defenders have been redesigned to evolve pure strategy, and a new network security simulation is devised which specifies network architecture and adds a temporal aspect. We also add a hill climber algorithm to evaluate the search space and justify the use of a coevolutionary algorithm.« less

Biogeography and environmental conditions shape bacteriophage-bacteria networks across the human microbiome

PubMed Central

Hannigan, Geoffrey D.; Duhaime, Melissa B.; Koutra, Danai

2018-01-01

Viruses and bacteria are critical components of the human microbiome and play important roles in health and disease. Most previous work has relied on studying bacteria and viruses independently, thereby reducing them to two separate communities. Such approaches are unable to capture how these microbial communities interact, such as through processes that maintain community robustness or allow phage-host populations to co-evolve. We implemented a network-based analytical approach to describe phage-bacteria network diversity throughout the human body. We built these community networks using a machine learning algorithm to predict which phages could infect which bacteria in a given microbiome. Our algorithm was applied to paired viral and bacterial metagenomic sequence sets from three previously published human cohorts. We organized the predicted interactions into networks that allowed us to evaluate phage-bacteria connectedness across the human body. We observed evidence that gut and skin network structures were person-specific and not conserved among cohabitating family members. High-fat diets appeared to be associated with less connected networks. Network structure differed between skin sites, with those exposed to the external environment being less connected and likely more susceptible to network degradation by microbial extinction events. This study quantified and contrasted the diversity of virome-microbiome networks across the human body and illustrated how environmental factors may influence phage-bacteria interactive dynamics. This work provides a baseline for future studies to better understand system perturbations, such as disease states, through ecological networks. PMID:29668682

Biogeography and environmental conditions shape bacteriophage-bacteria networks across the human microbiome.

PubMed

Hannigan, Geoffrey D; Duhaime, Melissa B; Koutra, Danai; Schloss, Patrick D

2018-04-01

Viruses and bacteria are critical components of the human microbiome and play important roles in health and disease. Most previous work has relied on studying bacteria and viruses independently, thereby reducing them to two separate communities. Such approaches are unable to capture how these microbial communities interact, such as through processes that maintain community robustness or allow phage-host populations to co-evolve. We implemented a network-based analytical approach to describe phage-bacteria network diversity throughout the human body. We built these community networks using a machine learning algorithm to predict which phages could infect which bacteria in a given microbiome. Our algorithm was applied to paired viral and bacterial metagenomic sequence sets from three previously published human cohorts. We organized the predicted interactions into networks that allowed us to evaluate phage-bacteria connectedness across the human body. We observed evidence that gut and skin network structures were person-specific and not conserved among cohabitating family members. High-fat diets appeared to be associated with less connected networks. Network structure differed between skin sites, with those exposed to the external environment being less connected and likely more susceptible to network degradation by microbial extinction events. This study quantified and contrasted the diversity of virome-microbiome networks across the human body and illustrated how environmental factors may influence phage-bacteria interactive dynamics. This work provides a baseline for future studies to better understand system perturbations, such as disease states, through ecological networks.

The repABC plasmids with quorum-regulated transfer systems in members of the Rhizobiales divide into two structurally and separately evolving groups

DOE PAGES

Wetzel, Margaret E.; Olsen, Gary J.; Chakravartty, Vandana; ...

2015-11-19

The large repABC plasmids of the order Rhizobiales with Class I quorum-regulated conjugative transfer systems often define the nature of the bacterium that harbors them. These otherwise diverse plasmids contain a core of highly conserved genes for replication and conjugation raising the question of their evolutionary relationships. In an analysis of 18 such plasmids these elements fall into two organizational classes, Group I and Group II, based on the sites at which cargo DNA is located. Cladograms constructed from proteins of the transfer and quorum-sensing components indicated that those of the Group I plasmids, while coevolving, have diverged from thosemore » coevolving proteins of the Group II plasmids. Moreover, within these groups the phylogenies of the proteins usually occupy similar, if not identical, tree topologies. Remarkably, such relationships were not seen among proteins of the replication system; although RepA and RepB coevolve, RepC does not. Nor do the replication proteins coevolve with the proteins of the transfer and quorum-sensing systems. Functional analysis was mostly consistent with phylogenies. TraR activated promoters from plasmids within its group, but not between groups and dimerized with TraR proteins from within but not between groups. However, oriT sequences, which are highly conserved, were processed by the transfer system of plasmids regardless of group. Here, we conclude that these plasmids diverged into two classes based on the locations at which cargo DNA is inserted, that the quorum-sensing and transfer functions are coevolving within but not between the two groups, and that this divergent evolution extends to function.« less

The repABC plasmids with quorum-regulated transfer systems in members of the Rhizobiales divide into two structurally and separately evolving groups

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

Wetzel, Margaret E.; Olsen, Gary J.; Chakravartty, Vandana

The large repABC plasmids of the order Rhizobiales with Class I quorum-regulated conjugative transfer systems often define the nature of the bacterium that harbors them. These otherwise diverse plasmids contain a core of highly conserved genes for replication and conjugation raising the question of their evolutionary relationships. In an analysis of 18 such plasmids these elements fall into two organizational classes, Group I and Group II, based on the sites at which cargo DNA is located. Cladograms constructed from proteins of the transfer and quorum-sensing components indicated that those of the Group I plasmids, while coevolving, have diverged from thosemore » coevolving proteins of the Group II plasmids. Moreover, within these groups the phylogenies of the proteins usually occupy similar, if not identical, tree topologies. Remarkably, such relationships were not seen among proteins of the replication system; although RepA and RepB coevolve, RepC does not. Nor do the replication proteins coevolve with the proteins of the transfer and quorum-sensing systems. Functional analysis was mostly consistent with phylogenies. TraR activated promoters from plasmids within its group, but not between groups and dimerized with TraR proteins from within but not between groups. However, oriT sequences, which are highly conserved, were processed by the transfer system of plasmids regardless of group. Here, we conclude that these plasmids diverged into two classes based on the locations at which cargo DNA is inserted, that the quorum-sensing and transfer functions are coevolving within but not between the two groups, and that this divergent evolution extends to function.« less

Coupling of link- and node-ordering in the coevolving voter model.

PubMed

Toruniewska, J; Kułakowski, K; Suchecki, K; Hołyst, J A

2017-10-01

We consider the process of reaching the final state in the coevolving voter model. There is a coevolution of state dynamics, where a node can copy a state from a random neighbor with probabilty 1-p and link dynamics, where a node can rewire its link to another node of the same state with probability p. That exhibits an absorbing transition to a frozen phase above a critical value of rewiring probability. Our analytical and numerical studies show that in the active phase mean values of magnetization of nodes n and links m tend to the same value that depends on initial conditions. In a similar way mean degrees of spins up and spins down become equal. The system obeys a special statistical conservation law since a linear combination of both types magnetizations averaged over many realizations starting from the same initial conditions is a constant of motion: Λ≡(1-p)μm(t)+pn(t)=const., where μ is the mean node degree. The final mean magnetization of nodes and links in the active phase is proportional to Λ while the final density of active links is a square function of Λ. If the rewiring probability is above a critical value and the system separates into disconnected domains, then the values of nodes and links magnetizations are not the same and final mean degrees of spins up and spins down can be different.

Coevolved Mutations Reveal Distinct Architectures for Two Core Proteins in the Bacterial Flagellar Motor

PubMed Central

Pandini, Alessandro; Kleinjung, Jens; Rasool, Shafqat; Khan, Shahid

2015-01-01

Switching of bacterial flagellar rotation is caused by large domain movements of the FliG protein triggered by binding of the signal protein CheY to FliM. FliG and FliM form adjacent multi-subunit arrays within the basal body C-ring. The movements alter the interaction of the FliG C-terminal (FliGC) “torque” helix with the stator complexes. Atomic models based on the Salmonella entrovar C-ring electron microscopy reconstruction have implications for switching, but lack consensus on the relative locations of the FliG armadillo (ARM) domains (amino-terminal (FliGN), middle (FliGM) and FliGC) as well as changes during chemotaxis. The generality of the Salmonella model is challenged by the variation in motor morphology and response between species. We studied coevolved residue mutations to determine the unifying elements of switch architecture. Residue interactions, measured by their coevolution, were formalized as a network, guided by structural data. Our measurements reveal a common design with dedicated switch and motor modules. The FliM middle domain (FliMM) has extensive connectivity most simply explained by conserved intra and inter-subunit contacts. In contrast, FliG has patchy, complex architecture. Conserved structural motifs form interacting nodes in the coevolution network that wire FliMM to the FliGC C-terminal, four-helix motor module (C3-6). FliG C3-6 coevolution is organized around the torque helix, differently from other ARM domains. The nodes form separated, surface-proximal patches that are targeted by deleterious mutations as in other allosteric systems. The dominant node is formed by the EHPQ motif at the FliMMFliGM contact interface and adjacent helix residues at a central location within FliGM. The node interacts with nodes in the N-terminal FliGc α-helix triad (ARM-C) and FliGN. ARM-C, separated from C3-6 by the MFVF motif, has poor intra-network connectivity consistent with its variable orientation revealed by structural data. ARM-C could be the convertor element that provides mechanistic and species diversity. PMID:26561852

Success of cuckoo catfish brood parasitism reflects coevolutionary history and individual experience of their cichlid hosts

PubMed Central

Polačik, Matej; Smith, Carl; Honza, Marcel; Reichard, Martin

2018-01-01

Obligate brood parasites manipulate other species into raising their offspring. Avian and insect brood parasitic systems demonstrate how interacting species engage in reciprocal coevolutionary arms races through behavioral and morphological adaptations and counteradaptations. Mouthbrooding cichlid fishes are renowned for their remarkable evolutionary radiations and complex behaviors. In Lake Tanganyika, mouthbrooding cichlids are exploited by the only obligate nonavian vertebrate brood parasite, the cuckoo catfish Synodontis multipunctatus. We show that coevolutionary history and individual learning both have a major impact on the success of cuckoo catfish parasitism between coevolved sympatric and evolutionarily naïve allopatric cichlid species. The rate of cuckoo catfish parasitism in coevolved Tanganyikan hosts was 3 to 11 times lower than in evolutionarily naïve cichlids. Moreover, using experimental infections, we demonstrate that parasite egg rejection in sympatric hosts was much higher, leading to seven times greater parasite survival in evolutionarily naïve than sympatric hosts. However, a high rejection frequency of parasitic catfish eggs by coevolved sympatric hosts came at a cost of increased rejection of their own eggs. A significant cost of catfish parasitism was universal, except for coevolved sympatric cichlid species with previous experience of catfish parasitism, demonstrating that learning and individual experience both contribute to a successful host response. PMID:29732407

Analysis on the urban street network of Korea: Connections between topology and meta-information

NASA Astrophysics Data System (ADS)

Lee, Byoung-Hwa; Jung, Woo-Sung

2018-05-01

Cities consist of infrastructure that enables transportation, which can be considered as topology in abstract terms. Once cities are physically organized in terms of infrastructure, people interact with each other to form the values, which can be regarded as the meta-information of the cities. The topology and meta-information coevolve together as the cities are developed. In this study, we investigate the relationship between the topology and meta-information for a street network, which has aspects of both a complex network and planar graph. The degree of organization of a street structure determines the efficiency and productivity of the city in that they act as blood vessels to transport people, goods, and information. We analyze the topological aspect of a street network using centralities including the betweenness, closeness, straightness, and information. We classify the cities into several groups that share common meta-information based on the centrality, indicating that the topological factor of the street structure is closely related to meta-information through coevolution. We also obtain the coevolution in the planned cities using the regularity. Another footprint is the relation between the street segment length and the population, which shows the sublinear scaling.

Designing a network of critical zone observatories to explore the living skin of the terrestrial Earth

NASA Astrophysics Data System (ADS)

Brantley, Susan L.; McDowell, William H.; Dietrich, William E.; White, Timothy S.; Kumar, Praveen; Anderson, Suzanne P.; Chorover, Jon; Lohse, Kathleen Ann; Bales, Roger C.; Richter, Daniel D.; Grant, Gordon; Gaillardet, Jérôme

2017-12-01

The critical zone (CZ), the dynamic living skin of the Earth, extends from the top of the vegetative canopy through the soil and down to fresh bedrock and the bottom of the groundwater. All humans live in and depend on the CZ. This zone has three co-evolving surfaces: the top of the vegetative canopy, the ground surface, and a deep subsurface below which Earth's materials are unweathered. The network of nine CZ observatories supported by the US National Science Foundation has made advances in three broad areas of CZ research relating to the co-evolving surfaces. First, monitoring has revealed how natural and anthropogenic inputs at the vegetation canopy and ground surface cause subsurface responses in water, regolith structure, minerals, and biotic activity to considerable depths. This response, in turn, impacts aboveground biota and climate. Second, drilling and geophysical imaging now reveal how the deep subsurface of the CZ varies across landscapes, which in turn influences aboveground ecosystems. Third, several new mechanistic models now provide quantitative predictions of the spatial structure of the subsurface of the CZ.Many countries fund critical zone observatories (CZOs) to measure the fluxes of solutes, water, energy, gases, and sediments in the CZ and some relate these observations to the histories of those fluxes recorded in landforms, biota, soils, sediments, and rocks. Each US observatory has succeeded in (i) synthesizing research across disciplines into convergent approaches; (ii) providing long-term measurements to compare across sites; (iii) testing and developing models; (iv) collecting and measuring baseline data for comparison to catastrophic events; (v) stimulating new process-based hypotheses; (vi) catalyzing development of new techniques and instrumentation; (vii) informing the public about the CZ; (viii) mentoring students and teaching about emerging multidisciplinary CZ science; and (ix) discovering new insights about the CZ. Many of these activities can only be accomplished with observatories. Here we review the CZO enterprise in the United States and identify how such observatories could operate in the future as a network designed to generate critical scientific insights. Specifically, we recognize the need for the network to study network-level questions, expand the environments under investigation, accommodate both hypothesis testing and monitoring, and involve more stakeholders. We propose a driving question for future CZ science and a hubs-and-campaigns model to address that question and target the CZ as one unit. Only with such integrative efforts will we learn to steward the life-sustaining critical zone now and into the future.

Coevolving agent strategies and network topology for the public goods games

NASA Astrophysics Data System (ADS)

Zhang, C. Y.; Zhang, J. L.; Xie, G. M.; Wang, L.

2011-03-01

Much of human cooperation remains an evolutionary riddle. Coevolutionary public goods games in structured populations are studied where players can change from an unproductive public goods game to a productive one, by evaluating the productivity of the public goods games. In our model, each individual participates in games organized by its neighborhood plus by itself. Coevolution here refers to an evolutionary process entailing both deletion of existing links and addition of new links between agents that accompanies the evolution of their strategies. Furthermore, we investigate the effects of time scale separation of strategy and structure on cooperation level. This study presents the following: Foremost, we observe that high cooperation levels in public goods interactions are attained by the entangled coevolution of strategy and structure. Presented results also confirm that the resulting networks show many features of real systems, such as cooperative behavior and hierarchical clustering. The heterogeneity of the interaction network is held responsible for the observed promotion of cooperation. We hope our work may offer an explanation for the origin of large-scale cooperative behavior among unrelated individuals.

Interrogation of Mammalian Protein Complex Structure, Function, and Membership Using Genome-Scale Fitness Screens.

PubMed

Pan, Joshua; Meyers, Robin M; Michel, Brittany C; Mashtalir, Nazar; Sizemore, Ann E; Wells, Jonathan N; Cassel, Seth H; Vazquez, Francisca; Weir, Barbara A; Hahn, William C; Marsh, Joseph A; Tsherniak, Aviad; Kadoch, Cigall

2018-05-23

Protein complexes are assemblies of subunits that have co-evolved to execute one or many coordinated functions in the cellular environment. Functional annotation of mammalian protein complexes is critical to understanding biological processes, as well as disease mechanisms. Here, we used genetic co-essentiality derived from genome-scale RNAi- and CRISPR-Cas9-based fitness screens performed across hundreds of human cancer cell lines to assign measures of functional similarity. From these measures, we systematically built and characterized functional similarity networks that recapitulate known structural and functional features of well-studied protein complexes and resolve novel functional modules within complexes lacking structural resolution, such as the mammalian SWI/SNF complex. Finally, by integrating functional networks with large protein-protein interaction networks, we discovered novel protein complexes involving recently evolved genes of unknown function. Taken together, these findings demonstrate the utility of genetic perturbation screens alone, and in combination with large-scale biophysical data, to enhance our understanding of mammalian protein complexes in normal and disease states. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Chemical camouflage: a key process in shaping an ant-treehopper and fig-fig wasp mutualistic network.

PubMed

Wang, Bo; Lu, Min; Cook, James M; Yang, Da-Rong; Dunn, Derek W; Wang, Rui-Wu

2018-01-30

Different types of mutualisms may interact, co-evolve and form complex networks of interdependences, but how species interact in networks of a mutualistic community and maintain its stability remains unclear. In a mutualistic network between treehoppers-weaver ants and fig-pollinating wasps, we found that the cuticular hydrocarbons of the treehoppers are more similar to the surface chemical profiles of fig inflorescence branches (FIB) than the cuticular hydrocarbons of the fig wasps. Behavioral assays showed that the cuticular hydrocarbons from both treehoppers and FIBs reduce the propensity of weaver ants to attack treehoppers even in the absence of honeydew rewards, suggesting that chemical camouflage helps enforce the mutualism between weaver ants and treehoppers. High levels of weaver ant and treehopper abundances help maintain the dominance of pollinating fig wasps in the fig wasp community and also increase fig seed production, as a result of discriminative predation and disturbance by weaver ants of ovipositing non-pollinating fig wasps (NPFWs). Ants therefore help preserve this fig-pollinating wasp mutualism from over exploitation by NPFWs. Our results imply that in this mutualistic network chemical camouflage plays a decisive role in regulating the behavior of a key species and indirectly shaping the architecture of complex arthropod-plant interactions.

Mechanical Network in Titin Immunoglobulin from Force Distribution Analysis

PubMed Central

Wilmanns, Matthias; Gräter, Frauke

2009-01-01

The role of mechanical force in cellular processes is increasingly revealed by single molecule experiments and simulations of force-induced transitions in proteins. How the applied force propagates within proteins determines their mechanical behavior yet remains largely unknown. We present a new method based on molecular dynamics simulations to disclose the distribution of strain in protein structures, here for the newly determined high-resolution crystal structure of I27, a titin immunoglobulin (IG) domain. We obtain a sparse, spatially connected, and highly anisotropic mechanical network. This allows us to detect load-bearing motifs composed of interstrand hydrogen bonds and hydrophobic core interactions, including parts distal to the site to which force was applied. The role of the force distribution pattern for mechanical stability is tested by in silico unfolding of I27 mutants. We then compare the observed force pattern to the sparse network of coevolved residues found in this family. We find a remarkable overlap, suggesting the force distribution to reflect constraints for the evolutionary design of mechanical resistance in the IG family. The force distribution analysis provides a molecular interpretation of coevolution and opens the road to the study of the mechanism of signal propagation in proteins in general. PMID:19282960

Opinion formation on multiplex scale-free networks

NASA Astrophysics Data System (ADS)

Nguyen, Vu Xuan; Xiao, Gaoxi; Xu, Xin-Jian; Li, Guoqi; Wang, Zhen

2018-01-01

Most individuals, if not all, live in various social networks. The formation of opinion systems is an outcome of social interactions and information propagation occurring in such networks. We study the opinion formation with a new rule of pairwise interactions in the novel version of the well-known Deffuant model on multiplex networks composed of two layers, each of which is a scale-free network. It is found that in a duplex network composed of two identical layers, the presence of the multiplexity helps either diminish or enhance opinion diversity depending on the relative magnitudes of tolerance ranges characterizing the degree of openness/tolerance on both layers: there is a steady separation between different regions of tolerance range values on two network layers where multiplexity plays two different roles, respectively. Additionally, the two critical tolerance ranges follow a one-sum rule; that is, each of the layers reaches a complete consensus only if the sum of the tolerance ranges on the two layers is greater than a constant approximately equaling 1, the double of the critical bound on a corresponding isolated network. A further investigation of the coupling between constituent layers quantified by a link overlap parameter reveals that as the layers are loosely coupled, the two opinion systems co-evolve independently, but when the inter-layer coupling is sufficiently strong, a monotonic behavior is observed: an increase in the tolerance range of a layer causes a decline in the opinion diversity on the other layer regardless of the magnitudes of tolerance ranges associated with the layers in question.

Meta-food-chains as a many-layer epidemic process on networks

NASA Astrophysics Data System (ADS)

Barter, Edmund; Gross, Thilo

2016-02-01

Notable recent works have focused on the multilayer properties of coevolving diseases. We point out that very similar systems play an important role in population ecology. Specifically we study a meta-food-web model that was recently proposed by Pillai et al. [Theor. Ecol. 3, 223 (2009), 10.1007/s12080-009-0065-1]. This model describes a network of species connected by feeding interactions, which spread over a network of spatial patches. Focusing on the essential case, where the network of feeding interactions is a chain, we develop an analytical approach for the computation of the degree distributions of colonized spatial patches for the different species in the chain. This framework allows us to address ecologically relevant questions. Considering configuration model ensembles of spatial networks, we find that there is an upper bound for the fraction of patches that a given species can occupy, which depends only on the networks mean degree. For a given mean degree there is then an optimal degree distribution that comes closest to the upper bound. Notably scale-free degree distributions perform worse than more homogeneous degree distributions if the mean degree is sufficiently high. Because species experience the underlying network differently the optimal degree distribution for one particular species is generally not the optimal distribution for the other species in the same food web. These results are of interest for conservation ecology, where, for instance, the task of selecting areas of old-growth forest to preserve in an agricultural landscape, amounts to the design of a patch network.

SNP-VISTA: An Interactive SNPs Visualization Tool

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

Shah, Nameeta; Teplitsky, Michael V.; Pennacchio, Len A.

2005-07-05

Recent advances in sequencing technologies promise better diagnostics for many diseases as well as better understanding of evolution of microbial populations. Single Nucleotide Polymorphisms(SNPs) are established genetic markers that aid in the identification of loci affecting quantitative traits and/or disease in a wide variety of eukaryotic species. With today's technological capabilities, it is possible to re-sequence a large set of appropriate candidate genes in individuals with a given disease and then screen for causative mutations.In addition, SNPs have been used extensively in efforts to study the evolution of microbial populations, and the recent application of random shotgun sequencing to environmentalmore » samples makes possible more extensive SNP analysis of co-occurring and co-evolving microbial populations. The program is available at http://genome.lbl.gov/vista/snpvista.« less

Zealotry effects on opinion dynamics in the adaptive voter model

NASA Astrophysics Data System (ADS)

Klamser, Pascal P.; Wiedermann, Marc; Donges, Jonathan F.; Donner, Reik V.

2017-11-01

The adaptive voter model has been widely studied as a conceptual model for opinion formation processes on time-evolving social networks. Past studies on the effect of zealots, i.e., nodes aiming to spread their fixed opinion throughout the system, only considered the voter model on a static network. Here we extend the study of zealotry to the case of an adaptive network topology co-evolving with the state of the nodes and investigate opinion spreading induced by zealots depending on their initial density and connectedness. Numerical simulations reveal that below the fragmentation threshold a low density of zealots is sufficient to spread their opinion to the whole network. Beyond the transition point, zealots must exhibit an increased degree as compared to ordinary nodes for an efficient spreading of their opinion. We verify the numerical findings using a mean-field approximation of the model yielding a low-dimensional set of coupled ordinary differential equations. Our results imply that the spreading of the zealots' opinion in the adaptive voter model is strongly dependent on the link rewiring probability and the average degree of normal nodes in comparison with that of the zealots. In order to avoid a complete dominance of the zealots' opinion, there are two possible strategies for the remaining nodes: adjusting the probability of rewiring and/or the number of connections with other nodes, respectively.

Conservation analysis and decomposition of residue correlation networks in the phospholipase A2 superfamily (PLA2s): Insights into the structure-function relationships of snake venom toxins.

PubMed

Oliveira, Alberto; Bleicher, Lucas; Schrago, Carlos G; Silva Junior, Floriano Paes

2018-05-01

Phospholipases A2 (PLA 2 s) comprise a superfamily of glycerophospholipids hydrolyzing enzymes present in many organisms in nature, whose catalytic activity was majorly unveiled by analysis of snake venoms. The latter have pharmaceutical and biotechnological interests and can be divided into different functional sub-classes. Our goal was to identify important residues and their relation to the functional and class-specific characteristics in the PLA 2 s family with special emphasis on snake venom PLA 2 s (svPLA 2 s). We identified such residues by conservation analysis and decomposition of residue coevolution networks (DRCN), annotated the results based on the available literature on PLA 2 s, structural analysis and molecular dynamics simulations, and related the results to the phylogenetic distribution of these proteins. A filtered alignment of PLA 2 s revealed 14 highly conserved positions and 3 sets of coevolved residues, which were annotated according to their structural or functional role. These residues are mostly involved in ligand binding and catalysis, calcium-binding, the formation of disulfide bridges and a hydrophobic cluster close to the binding site. An independent validation of the inference of structure-function relationships from our co-evolution analysis on the svPLA2s family was obtained by the analysis of the pattern of selection acting on the Viperidae and Elapidae lineages. Additionally, a molecular dynamics simulation on the Lys49 PLA 2 from Agkistrodon contortrix laticinctus was carried out to further investigate the correlation of the Lys49-Glu69 pair. Our results suggest this configuration can result in a novel conformation where the binding cavity collapses due to the approximation of two loops caused by a strong salt bridge between Glu69 and Arg34. Finally, phylogenetic analysis indicated a correlation between the presence of residues in the coevolved sets found in this analysis and the clade localization. The results provide a guide for important positions in the family of PLA 2 s, and potential new objects of investigation. Copyright © 2018 Elsevier Ltd. All rights reserved.

Search for Directed Networks by Different Random Walk Strategies

NASA Astrophysics Data System (ADS)

Zhu, Zi-Qi; Jin, Xiao-Ling; Huang, Zhi-Long

2012-03-01

A comparative study is carried out on the efficiency of five different random walk strategies searching on directed networks constructed based on several typical complex networks. Due to the difference in search efficiency of the strategies rooted in network clustering, the clustering coefficient in a random walker's eye on directed networks is defined and computed to be half of the corresponding undirected networks. The search processes are performed on the directed networks based on Erdös—Rényi model, Watts—Strogatz model, Barabási—Albert model and clustered scale-free network model. It is found that self-avoiding random walk strategy is the best search strategy for such directed networks. Compared to unrestricted random walk strategy, path-iteration-avoiding random walks can also make the search process much more efficient. However, no-triangle-loop and no-quadrangle-loop random walks do not improve the search efficiency as expected, which is different from those on undirected networks since the clustering coefficient of directed networks are smaller than that of undirected networks.

Modeling growth and dissemination of lymphoma in a co-evolving lymph node: a diffuse-domain approach

NASA Astrophysics Data System (ADS)

Chuang, Yao-Li; Cristini, Vittorio; Chen, Ying; Li, Xiangrong; Frieboes, Hermann; Lowengrub, John

2013-03-01

While partial differential equation models of tumor growth have successfully described various spatiotemporal phenomena observed for in-vitro tumor spheroid experiments, one challenge towards taking these models to further study in-vivo tumors is that instead of relatively static tissue culture with regular boundary conditions, in-vivo tumors are often confined in organ tissues that co-evolve with the tumor growth. Here we adopt a recently developed diffuse-domain method to account for the co-evolving domain boundaries, adapting our previous in-vitro tumor model for the development of lymphoma encapsulated in a lymph node, which may swell or shrink due to proliferation and dissemination of lymphoma cells and treatment by chemotherapy. We use the model to study the induced spatial heterogeneity, which may arise as an emerging phenomenon in experimental observations and model analysis. Spatial heterogeneity is believed to lead to tumor infiltration patterns and reduce the efficacy of chemotherapy, leaving residuals that cause cancer relapse after the treatment. Understanding the spatiotemporal evolution of in-vivo tumors can be an essential step towards more effective strategies of curing cancer. Supported by NIH-PSOC grant 1U54CA143907-01.

Floral scent contributes to interaction specificity in coevolving plants and their insect pollinators.

PubMed

Friberg, Magne; Schwind, Christopher; Roark, Lindsey C; Raguso, Robert A; Thompson, John N

2014-09-01

Chemical defenses, repellents, and attractants are important shapers of species interactions. Chemical attractants could contribute to the divergence of coevolving plant-insect interactions, if pollinators are especially responsive to signals from the local plant species. We experimentally investigated patterns of daily floral scent production in three Lithophragma species (Saxifragaceae) that are geographically isolated and tested how scent divergence affects attraction of their major pollinator-the floral parasitic moth Greya politella (Prodoxidae). These moths oviposit through the corolla while simultaneously pollinating the flower with pollen adhering to the abdomen. The complex and species-specific floral scent profiles were emitted in higher amounts during the day, when these day-flying moths are active. There was minimal divergence found in petal color, which is another potential floral attractant. Female moths responded most strongly to scent from their local host species in olfactometer bioassays, and were more likely to oviposit in, and thereby pollinate, their local host species in no-choice trials. The results suggest that floral scent is an important attractant in this interaction. Local specialization in the pollinator response to a highly specific plant chemistry, thus, has the potential to contribute importantly to patterns of interaction specificity among coevolving plants and highly specialized pollinators.

New Measurement for Correlation of Co-evolution Relationship of Subsequences in Protein.

PubMed

Gao, Hongyun; Yu, Xiaoqing; Dou, Yongchao; Wang, Jun

2015-12-01

Many computational tools have been developed to measure the protein residues co-evolution. Most of them only focus on co-evolution for pairwise residues in a protein sequence. However, number of residues participate in co-evolution might be multiple. And some co-evolved residues are clustered in several distinct regions in primary structure. Therefore, the co-evolution among the adjacent residues and the correlation between the distinct regions offer insights into function and evolution of the protein and residues. Subsequence is used to represent the adjacent multiple residues in one distinct region. In the paper, co-evolution relationship in each subsequence is represented by mutual information matrix (MIM). Then, Pearson's correlation coefficient: R value is developed to measure the similarity correlation of two MIMs. MSAs from Catalytic Data Base (Catalytic Site Atlas, CSA) are used for testing. R value characterizes a specific class of residues. In contrast to individual pairwise co-evolved residues, adjacent residues without high individual MI values are found since the co-evolved relationship among them is similar to that among another set of adjacent residues. These subsequences possess some flexibility in the composition of side chains, such as the catalyzed environment.

Translational regulation of ribosomal protein S15 drives characteristic patterns of protein-mRNA epistasis.

PubMed

Mallik, Saurav; Basu, Sudipto; Hait, Suman; Kundu, Sudip

2018-04-21

Do coding and regulatory segments of a gene co-evolve with each-other? Seeking answers to this question, here we analyze the case of Escherichia coli ribosomal protein S15, that represses its own translation by specifically binding its messenger RNA (rpsO mRNA) and stabilizing a pseudoknot structure at the upstream untranslated region, thus trapping the ribosome into an incomplete translation initiation complex. In the absence of S15, ribosomal protein S1 recognizes rpsO and promotes translation by melting this very pseudoknot. We employ a robust statistical method to detect signatures of positive epistasis between residue site pairs and find that biophysical constraints of translational regulation (S15-rpsO and S1-rpsO recognition, S15-mediated rpsO structural rearrangement, and S1-mediated melting) are strong predictors of positive epistasis. Transforming the epistatic pairs into a network, we find that signatures of two different, but interconnected regulatory cascades are imprinted in the sequence-space and can be captured in terms of two dense network modules that are sparsely connected to each other. This network topology further reflects a general principle of how functionally coupled components of biological networks are interconnected. These results depict a model case, where translational regulation drives characteristic residue-level epistasis-not only between a protein and its own mRNA but also between a protein and the mRNA of an entirely different protein. © 2018 Wiley Periodicals, Inc.

Specialization in Plant-Hummingbird Networks Is Associated with Species Richness, Contemporary Precipitation and Quaternary Climate-Change Velocity

PubMed Central

Dalsgaard, Bo; Magård, Else; Fjeldså, Jon; Martín González, Ana M.; Rahbek, Carsten; Olesen, Jens M.; Ollerton, Jeff; Alarcón, Ruben; Cardoso Araujo, Andrea; Cotton, Peter A.; Lara, Carlos; Machado, Caio Graco; Sazima, Ivan; Sazima, Marlies; Timmermann, Allan; Watts, Stella; Sandel, Brody; Sutherland, William J.; Svenning, Jens-Christian

2011-01-01

Large-scale geographical patterns of biotic specialization and the underlying drivers are poorly understood, but it is widely believed that climate plays an important role in determining specialization. As climate-driven range dynamics should diminish local adaptations and favor generalization, one hypothesis is that contemporary biotic specialization is determined by the degree of past climatic instability, primarily Quaternary climate-change velocity. Other prominent hypotheses predict that either contemporary climate or species richness affect biotic specialization. To gain insight into geographical patterns of contemporary biotic specialization and its drivers, we use network analysis to determine the degree of specialization in plant-hummingbird mutualistic networks sampled at 31 localities, spanning a wide range of climate regimes across the Americas. We found greater biotic specialization at lower latitudes, with latitude explaining 20–22% of the spatial variation in plant-hummingbird specialization. Potential drivers of specialization - contemporary climate, Quaternary climate-change velocity, and species richness - had superior explanatory power, together explaining 53–64% of the variation in specialization. Notably, our data provides empirical evidence for the hypothesized roles of species richness, contemporary precipitation and Quaternary climate-change velocity as key predictors of biotic specialization, whereas contemporary temperature and seasonality seem unimportant in determining specialization. These results suggest that both ecological and evolutionary processes at Quaternary time scales can be important in driving large-scale geographical patterns of contemporary biotic specialization, at least for co-evolved systems such as plant-hummingbird networks. PMID:21998716

Percolation of localized attack on isolated and interdependent random networks

NASA Astrophysics Data System (ADS)

Shao, Shuai; Huang, Xuqing; Stanley, H. Eugene; Havlin, Shlomo

2014-03-01

Percolation properties of isolated and interdependent random networks have been investigated extensively. The focus of these studies has been on random attacks where each node in network is attacked with the same probability or targeted attack where each node is attacked with a probability being a function of its centrality, such as degree. Here we discuss a new type of realistic attacks which we call a localized attack where a group of neighboring nodes in the networks are attacked. We attack a randomly chosen node, its neighbors, and its neighbor of neighbors and so on, until removing a fraction (1 - p) of the network. This type of attack reflects damages due to localized disasters, such as earthquakes, floods and war zones in real-world networks. We study, both analytically and by simulations the impact of localized attack on percolation properties of random networks with arbitrary degree distributions and discuss in detail random regular (RR) networks, Erdős-Rényi (ER) networks and scale-free (SF) networks. We extend and generalize our theoretical and simulation results of single isolated networks to networks formed of interdependent networks.

Quantifying randomness in real networks

NASA Astrophysics Data System (ADS)

Orsini, Chiara; Dankulov, Marija M.; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E.; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri

2015-10-01

Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.

Synchronization in Random Pulse Oscillator Networks

NASA Astrophysics Data System (ADS)

Brown, Kevin; Hermundstad, Ann

Motivated by synchronization phenomena in neural systems, we study synchronization of random networks of coupled pulse oscillators. We begin by considering binomial random networks whose nodes have intrinsic linear dynamics. We quantify order in the network spiking dynamics using a new measure: the normalized Lev-Zimpel complexity (LZC) of the nodes' spike trains. Starting from a globally-synchronized state, we see two broad classes of behaviors. In one (''temporally random''), the LZC is high and nodes spike independently with no coherent pattern. In another (''temporally regular''), the network does not globally synchronize but instead forms coherent, repeating population firing patterns with low LZC. No topological feature of the network reliably predicts whether an individual network will show temporally random or regular behavior; however, we find evidence that degree heterogeneity in binomial networks has a strong effect on the resulting state. To confirm these findings, we generate random networks with independently-adjustable degree mean and variance. We find that the likelihood of temporally-random behavior increases as degree variance increases. Our results indicate the subtle and complex relationship between network structure and dynamics.

Assembler: Efficient Discovery of Spatial Co-evolving Patterns in Massive Geo-sensory Data.

PubMed

Zhang, Chao; Zheng, Yu; Ma, Xiuli; Han, Jiawei

2015-08-01

Recent years have witnessed the wide proliferation of geo-sensory applications wherein a bundle of sensors are deployed at different locations to cooperatively monitor the target condition. Given massive geo-sensory data, we study the problem of mining spatial co-evolving patterns (SCPs), i.e ., groups of sensors that are spatially correlated and co-evolve frequently in their readings. SCP mining is of great importance to various real-world applications, yet it is challenging because (1) the truly interesting evolutions are often flooded by numerous trivial fluctuations in the geo-sensory time series; and (2) the pattern search space is extremely large due to the spatiotemporal combinatorial nature of SCP. In this paper, we propose a two-stage method called Assembler. In the first stage, Assembler filters trivial fluctuations using wavelet transform and detects frequent evolutions for individual sensors via a segment-and-group approach. In the second stage, Assembler generates SCPs by assembling the frequent evolutions of individual sensors. Leveraging the spatial constraint, it conceptually organizes all the SCPs into a novel structure called the SCP search tree, which facilitates the effective pruning of the search space to generate SCPs efficiently. Our experiments on both real and synthetic data sets show that Assembler is effective, efficient, and scalable.

Visualizing Complex Environments in the Geo- and BioSciences

NASA Astrophysics Data System (ADS)

Prabhu, A.; Fox, P. A.; Zhong, H.; Eleish, A.; Ma, X.; Zednik, S.; Morrison, S. M.; Moore, E. K.; Muscente, D.; Meyer, M.; Hazen, R. M.

2017-12-01

Earth's living and non-living components have co-evolved for 4 billion years through numerous positive and negative feedbacks. Earth and life scientists have amassed vast amounts of data in diverse fields related to planetary evolution through deep time-mineralogy and petrology, paleobiology and paleontology, paleotectonics and paleomagnetism, geochemistry and geochrononology, genomics and proteomics, and more. Integrating the data from these complimentary disciplines is very useful in gaining an understanding of the evolution of our planet's environment. The integrated data however, represent many extremely complex environments. In order to gain insights and make discoveries using this data, it is important for us to model and visualize these complex environments. As part of work in understanding the "Co-Evolution of Geo and Biospheres using Data Driven Methodologies," we have developed several visualizations to help represent the information stored in the datasets from complimentary disciplines. These visualizations include 2D and 3D force directed Networks, Chord Diagrams, 3D Klee Diagrams. Evolving Network Diagrams, Skyline Diagrams and Tree Diagrams. Combining these visualizations with the results of machine learning and data analysis methods leads to a powerful way to discover patterns and relationships about the Earth's past and today's changing environment.

Bursts of Vertex Activation and Epidemics in Evolving Networks

PubMed Central

Rocha, Luis E. C.; Blondel, Vincent D.

2013-01-01

The dynamic nature of contact patterns creates diverse temporal structures. In particular, empirical studies have shown that contact patterns follow heterogeneous inter-event time intervals, meaning that periods of high activity are followed by long periods of inactivity. To investigate the impact of these heterogeneities in the spread of infection from a theoretical perspective, we propose a stochastic model to generate temporal networks where vertices make instantaneous contacts following heterogeneous inter-event intervals, and may leave and enter the system. We study how these properties affect the prevalence of an infection and estimate , the number of secondary infections of an infectious individual in a completely susceptible population, by modeling simulated infections (SI and SIR) that co-evolve with the network structure. We find that heterogeneous contact patterns cause earlier and larger epidemics in the SIR model in comparison to homogeneous scenarios for a vast range of parameter values, while smaller epidemics may happen in some combinations of parameters. In the case of SI and heterogeneous patterns, the epidemics develop faster in the earlier stages followed by a slowdown in the asymptotic limit. For increasing vertex turnover rates, heterogeneous patterns generally cause higher prevalence in comparison to homogeneous scenarios with the same average inter-event interval. We find that is generally higher for heterogeneous patterns, except for sufficiently large infection duration and transmission probability. PMID:23555211

Situating Green Infrastructure in Context: A Framework for Adaptive Socio-Hydrology in Cities.

PubMed

Schifman, L A; Herrmann, D L; Shuster, W D; Ossola, A; Garmestani, A; Hopton, M E

2017-12-01

Management of urban hydrologic processes using green infrastructure (GI) has largely focused on stormwater management. Thus, design and implementation of GI usually rely on physical site characteristics and local rainfall patterns, and do not typically account for human or social dimensions. This traditional approach leads to highly centralized stormwater management in a disconnected urban landscape, and can deemphasize additional benefits that GI offers, such as increased property value, greenspace aesthetics, heat island amelioration, carbon sequestration, and habitat for biodiversity. We propose a Framework for Adaptive Socio-Hydrology (FrASH) in which GI planning and implementation moves from a purely hydrology-driven perspective to an integrated socio-hydrological approach. This allows for an iterative, multifaceted decision-making process that would enable a network of stakeholders to collaboratively set a dynamic, context-guided project plan for the installation of GI, rather than a 'one-size-fits-all' installation. We explain how different sectors (e.g., governance, non-governmental organizations, academia, and industry) can create a connected network of organizations that work towards a common goal. Through a graphical Chambered Nautilus model, FrASH is experimentally applied to contrasting GI case studies and shows that this multi-stakeholder, connected, de-centralized network with a co-evolving decision-making project plan results in enhanced multi-functionality, potentially allowing for the management of resilience in urban systems at multiple scales.

Situating Green Infrastructure in Context: A Framework for Adaptive Socio-Hydrology in Cities

NASA Astrophysics Data System (ADS)

Schifman, L. A.; Herrmann, D. L.; Shuster, W. D.; Ossola, A.; Garmestani, A.; Hopton, M. E.

2017-12-01

Management of urban hydrologic processes using green infrastructure (GI) has largely focused on storm water management. Thus, design and implementation of GI usually rely on physical site characteristics and local rainfall patterns, and do not typically account for human or social dimensions. This traditional approach leads to highly centralized storm water management in a disconnected urban landscape and can deemphasize additional benefits that GI offers, such as increased property value, greenspace aesthetics, heat island amelioration, carbon sequestration, and habitat for biodiversity. We propose the Framework for Adaptive Socio-Hydrology (FrASH) in which GI planning and implementation moves from a purely hydrology-driven perspective to an integrated sociohydrological approach. This allows for an iterative, multifaceted decision-making process that would enable a network of stakeholders to collaboratively set a dynamic, context-guided project plan for the installation of GI, rather than a "one-size-fits-all" installation. We explain how different sectors (e.g., governance, nongovernmental organizations, communities, academia, and industry) can create a connected network of organizations that work toward a common goal. Through a graphical Chambered Nautilus model, FrASH is experimentally applied to contrasting GI case studies and shows that this multistakeholder, connected, decentralized network with a coevolving decision-making project plan results in enhanced multifunctionality, potentially allowing for the management of resilience in urban systems at multiple scales.

Autonomic and Coevolutionary Sensor Networking

NASA Astrophysics Data System (ADS)

Boonma, Pruet; Suzuki, Junichi

(WSNs) applications are often required to balance the tradeoffs among conflicting operational objectives (e.g., latency and power consumption) and operate at an optimal tradeoff. This chapter proposes and evaluates a architecture, called BiSNET/e, which allows WSN applications to overcome this issue. BiSNET/e is designed to support three major types of WSN applications: , and hybrid applications. Each application is implemented as a decentralized group of, which is analogous to a bee colony (application) consisting of bees (agents). Agents collect sensor data or detect an event (a significant change in sensor reading) on individual nodes, and carry sensor data to base stations. They perform these data collection and event detection functionalities by sensing their surrounding network conditions and adaptively invoking behaviors such as pheromone emission, reproduction, migration, swarming and death. Each agent has its own behavior policy, as a set of genes, which defines how to invoke its behaviors. BiSNET/e allows agents to evolve their behavior policies (genes) across generations and autonomously adapt their performance to given objectives. Simulation results demonstrate that, in all three types of applications, agents evolve to find optimal tradeoffs among conflicting objectives and adapt to dynamic network conditions such as traffic fluctuations and node failures/additions. Simulation results also illustrate that, in hybrid applications, data collection agents and event detection agents coevolve to augment their adaptability and performance.

Cross over of recurrence networks to random graphs and random geometric graphs

NASA Astrophysics Data System (ADS)

Jacob, Rinku; Harikrishnan, K. P.; Misra, R.; Ambika, G.

2017-02-01

Recurrence networks are complex networks constructed from the time series of chaotic dynamical systems where the connection between two nodes is limited by the recurrence threshold. This condition makes the topology of every recurrence network unique with the degree distribution determined by the probability density variations of the representative attractor from which it is constructed. Here we numerically investigate the properties of recurrence networks from standard low-dimensional chaotic attractors using some basic network measures and show how the recurrence networks are different from random and scale-free networks. In particular, we show that all recurrence networks can cross over to random geometric graphs by adding sufficient amount of noise to the time series and into the classical random graphs by increasing the range of interaction to the system size. We also highlight the effectiveness of a combined plot of characteristic path length and clustering coefficient in capturing the small changes in the network characteristics.

Testing the structure of earthquake networks from multivariate time series of successive main shocks in Greece

NASA Astrophysics Data System (ADS)

Chorozoglou, D.; Kugiumtzis, D.; Papadimitriou, E.

2018-06-01

The seismic hazard assessment in the area of Greece is attempted by studying the earthquake network structure, such as small-world and random. In this network, a node represents a seismic zone in the study area and a connection between two nodes is given by the correlation of the seismic activity of two zones. To investigate the network structure, and particularly the small-world property, the earthquake correlation network is compared with randomized ones. Simulations on multivariate time series of different length and number of variables show that for the construction of randomized networks the method randomizing the time series performs better than methods randomizing directly the original network connections. Based on the appropriate randomization method, the network approach is applied to time series of earthquakes that occurred between main shocks in the territory of Greece spanning the period 1999-2015. The characterization of networks on sliding time windows revealed that small-world structure emerges in the last time interval, shortly before the main shock.

A random spatial network model based on elementary postulates

USGS Publications Warehouse

Karlinger, Michael R.; Troutman, Brent M.

1989-01-01

A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

Randomizing Genome-Scale Metabolic Networks

PubMed Central

Samal, Areejit; Martin, Olivier C.

2011-01-01

Networks coming from protein-protein interactions, transcriptional regulation, signaling, or metabolism may appear to have “unusual” properties. To quantify this, it is appropriate to randomize the network and test the hypothesis that the network is not statistically different from expected in a motivated ensemble. However, when dealing with metabolic networks, the randomization of the network using edge exchange generates fictitious reactions that are biochemically meaningless. Here we provide several natural ensembles of randomized metabolic networks. A first constraint is to use valid biochemical reactions. Further constraints correspond to imposing appropriate functional constraints. We explain how to perform these randomizations with the help of Markov Chain Monte Carlo (MCMC) and show that they allow one to approach the properties of biological metabolic networks. The implication of the present work is that the observed global structural properties of real metabolic networks are likely to be the consequence of simple biochemical and functional constraints. PMID:21779409

Robust-yet-fragile nature of interdependent networks

NASA Astrophysics Data System (ADS)

Tan, Fei; Xia, Yongxiang; Wei, Zhi

2015-05-01

Interdependent networks have been shown to be extremely vulnerable based on the percolation model. Parshani et al. [Europhys. Lett. 92, 68002 (2010), 10.1209/0295-5075/92/68002] further indicated that the more intersimilar networks are, the more robust they are to random failures. When traffic load is considered, how do the coupling patterns impact cascading failures in interdependent networks? This question has been largely unexplored until now. In this paper, we address this question by investigating the robustness of interdependent Erdös-Rényi random graphs and Barabási-Albert scale-free networks under either random failures or intentional attacks. It is found that interdependent Erdös-Rényi random graphs are robust yet fragile under either random failures or intentional attacks. Interdependent Barabási-Albert scale-free networks, however, are only robust yet fragile under random failures but fragile under intentional attacks. We further analyze the interdependent communication network and power grid and achieve similar results. These results advance our understanding of how interdependency shapes network robustness.

Source Localization in Wireless Sensor Networks with Randomly Distributed Elements under Multipath Propagation Conditions

DTIC Science & Technology

2009-03-01

IN WIRELESS SENSOR NETWORKS WITH RANDOMLY DISTRIBUTED ELEMENTS UNDER MULTIPATH PROPAGATION CONDITIONS by Georgios Tsivgoulis March 2009...COVERED Engineer’s Thesis 4. TITLE Source Localization in Wireless Sensor Networks with Randomly Distributed Elements under Multipath Propagation...the non-line-of-sight information. 15. NUMBER OF PAGES 111 14. SUBJECT TERMS Wireless Sensor Network , Direction of Arrival, DOA, Random

Bridges in complex networks

NASA Astrophysics Data System (ADS)

Wu, Ang-Kun; Tian, Liang; Liu, Yang-Yu

2018-01-01

A bridge in a graph is an edge whose removal disconnects the graph and increases the number of connected components. We calculate the fraction of bridges in a wide range of real-world networks and their randomized counterparts. We find that real networks typically have more bridges than their completely randomized counterparts, but they have a fraction of bridges that is very similar to their degree-preserving randomizations. We define an edge centrality measure, called bridgeness, to quantify the importance of a bridge in damaging a network. We find that certain real networks have a very large average and variance of bridgeness compared to their degree-preserving randomizations and other real networks. Finally, we offer an analytical framework to calculate the bridge fraction and the average and variance of bridgeness for uncorrelated random networks with arbitrary degree distributions.

Percolation and epidemics in random clustered networks

NASA Astrophysics Data System (ADS)

Miller, Joel C.

2009-08-01

The social networks that infectious diseases spread along are typically clustered. Because of the close relation between percolation and epidemic spread, the behavior of percolation in such networks gives insight into infectious disease dynamics. A number of authors have studied percolation or epidemics in clustered networks, but the networks often contain preferential contacts in high degree nodes. We introduce a class of random clustered networks and a class of random unclustered networks with the same preferential mixing. Percolation in the clustered networks reduces the component sizes and increases the epidemic threshold compared to the unclustered networks.

Loss of functional diversity and network modularity in introduced plant–fungal symbioses

PubMed Central

Cooper, Jerry A.; Bufford, Jennifer L.; Hulme, Philip E.; Bates, Scott T.

2017-01-01

The introduction of alien plants into a new range can result in the loss of co-evolved symbiotic organisms, such as mycorrhizal fungi, that are essential for normal plant physiological functions. Prior studies of mycorrhizal associations in alien plants have tended to focus on individual plant species on a case-by-case basis. This approach limits broad scale understanding of functional shifts and changes in interaction network structure that may occur following introduction. Here we use two extensive datasets of plant–fungal interactions derived from fungal sporocarp observations and recorded plant hosts in two island archipelago nations: New Zealand (NZ) and the United Kingdom (UK). We found that the NZ dataset shows a lower functional diversity of fungal hyphal foraging strategies in mycorrhiza of alien when compared with native trees. Across species this resulted in fungal foraging strategies associated with alien trees being much more variable in functional composition compared with native trees, which had a strikingly similar functional composition. The UK data showed no functional difference in fungal associates of alien and native plant genera. Notwithstanding this, both the NZ and UK data showed a substantial difference in interaction network structure of alien trees compared with native trees. In both cases, fungal associates of native trees showed strong modularity, while fungal associates of alien trees generally integrated into a single large module. The results suggest a lower functional diversity (in one dataset) and a simplification of network structure (in both) as a result of introduction, potentially driven by either limited symbiont co-introductions or disruption of habitat as a driver of specificity due to nursery conditions, planting, or plant edaphic-niche expansion. Recognizing these shifts in function and network structure has important implications for plant invasions and facilitation of secondary invasions via shared mutualist populations. PMID:28039116

Random walks and diffusion on networks

NASA Astrophysics Data System (ADS)

Masuda, Naoki; Porter, Mason A.; Lambiotte, Renaud

2017-11-01

Random walks are ubiquitous in the sciences, and they are interesting from both theoretical and practical perspectives. They are one of the most fundamental types of stochastic processes; can be used to model numerous phenomena, including diffusion, interactions, and opinions among humans and animals; and can be used to extract information about important entities or dense groups of entities in a network. Random walks have been studied for many decades on both regular lattices and (especially in the last couple of decades) on networks with a variety of structures. In the present article, we survey the theory and applications of random walks on networks, restricting ourselves to simple cases of single and non-adaptive random walkers. We distinguish three main types of random walks: discrete-time random walks, node-centric continuous-time random walks, and edge-centric continuous-time random walks. We first briefly survey random walks on a line, and then we consider random walks on various types of networks. We extensively discuss applications of random walks, including ranking of nodes (e.g., PageRank), community detection, respondent-driven sampling, and opinion models such as voter models.

Detecting Coevolution in and among Protein Domains

PubMed Central

Yeang, Chen-Hsiang; Haussler, David

2007-01-01

Correlated changes of nucleic or amino acids have provided strong information about the structures and interactions of molecules. Despite the rich literature in coevolutionary sequence analysis, previous methods often have to trade off between generality, simplicity, phylogenetic information, and specific knowledge about interactions. Furthermore, despite the evidence of coevolution in selected protein families, a comprehensive screening of coevolution among all protein domains is still lacking. We propose an augmented continuous-time Markov process model for sequence coevolution. The model can handle different types of interactions, incorporate phylogenetic information and sequence substitution, has only one extra free parameter, and requires no knowledge about interaction rules. We employ this model to large-scale screenings on the entire protein domain database (Pfam). Strikingly, with 0.1 trillion tests executed, the majority of the inferred coevolving protein domains are functionally related, and the coevolving amino acid residues are spatially coupled. Moreover, many of the coevolving positions are located at functionally important sites of proteins/protein complexes, such as the subunit linkers of superoxide dismutase, the tRNA binding sites of ribosomes, the DNA binding region of RNA polymerase, and the active and ligand binding sites of various enzymes. The results suggest sequence coevolution manifests structural and functional constraints of proteins. The intricate relations between sequence coevolution and various selective constraints are worth pursuing at a deeper level. PMID:17983264

Two separate interfaces between the voltage sensor and pore are required for the function of voltage-dependent K(+) channels.

PubMed

Lee, Seok-Yong; Banerjee, Anirban; MacKinnon, Roderick

2009-03-03

Voltage-dependent K(+) (Kv) channels gate open in response to the membrane voltage. To further our understanding of how cell membrane voltage regulates the opening of a Kv channel, we have studied the protein interfaces that attach the voltage-sensor domains to the pore. In the crystal structure, three physical interfaces exist. Only two of these consist of amino acids that are co-evolved across the interface between voltage sensor and pore according to statistical coupling analysis of 360 Kv channel sequences. A first co-evolved interface is formed by the S4-S5 linkers (one from each of four voltage sensors), which form a cuff surrounding the S6-lined pore opening at the intracellular surface. The crystal structure and published mutational studies support the hypothesis that the S4-S5 linkers convert voltage-sensor motions directly into gate opening and closing. A second co-evolved interface forms a small contact surface between S1 of the voltage sensor and the pore helix near the extracellular surface. We demonstrate through mutagenesis that this interface is necessary for the function and/or structure of two different Kv channels. This second interface is well positioned to act as a second anchor point between the voltage sensor and the pore, thus allowing efficient transmission of conformational changes to the pore's gate.

Supermassive black holes do not correlate with galaxy disks or pseudobulges.

PubMed

Kormendy, John; Bender, R; Cornell, M E

2011-01-20

The masses of supermassive black holes are known to correlate with the properties of the bulge components of their host galaxies. In contrast, they seem not to correlate with galaxy disks. Disk-grown 'pseudobulges' are intermediate in properties between bulges and disks; it has been unclear whether they do or do not correlate with black holes in the same way that bulges do. At stake in this issue are conclusions about which parts of galaxies coevolve with black holes, possibly by being regulated by energy feedback from black holes. Here we report pseudobulge classifications for galaxies with dynamically detected black holes and combine them with recent measurements of velocity dispersions in the biggest bulgeless galaxies. These data confirm that black holes do not correlate with disks and show that they correlate little or not at all with pseudobulges. We suggest that there are two different modes of black-hole feeding. Black holes in bulges grow rapidly to high masses when mergers drive gas infall that feeds quasar-like events. In contrast, small black holes in bulgeless galaxies and in galaxies with pseudobulges grow as low-level Seyfert galaxies. Growth of the former is driven by global processes, so the biggest black holes coevolve with bulges, but growth of the latter is driven locally and stochastically, and they do not coevolve with disks and pseudobulges.

Dynamic defense and network randomization for computer systems

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

Chavez, Adrian R.; Stout, William M. S.; Hamlet, Jason R.

The various technologies presented herein relate to determining a network attack is taking place, and further to adjust one or more network parameters such that the network becomes dynamically configured. A plurality of machine learning algorithms are configured to recognize an active attack pattern. Notification of the attack can be generated, and knowledge gained from the detected attack pattern can be utilized to improve the knowledge of the algorithms to detect a subsequent attack vector(s). Further, network settings and application communications can be dynamically randomized, wherein artificial diversity converts control systems into moving targets that help mitigate the early reconnaissancemore » stages of an attack. An attack(s) based upon a known static address(es) of a critical infrastructure network device(s) can be mitigated by the dynamic randomization. Network parameters that can be randomized include IP addresses, application port numbers, paths data packets navigate through the network, application randomization, etc.« less

Damage spreading in spatial and small-world random Boolean networks

NASA Astrophysics Data System (ADS)

Lu, Qiming; Teuscher, Christof

2014-02-01

The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean networks (RBNs) are commonly used as a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other nonrandom connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the Hamming distance at very low connectivities (K¯≪1) and that the critical connectivity of stability Ks changes compared to random networks. At higher K¯, this scaling remains unchanged. We also show that the Hamming distance of spatially local networks scales with a power law as the system size N increases, but with a different exponent for local and small-world networks. The scaling arguments for small-world networks are obtained with respect to the system sizes and strength of spatially local connections. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.

Spectrum of walk matrix for Koch network and its application

NASA Astrophysics Data System (ADS)

Xie, Pinchen; Lin, Yuan; Zhang, Zhongzhi

2015-06-01

Various structural and dynamical properties of a network are encoded in the eigenvalues of walk matrix describing random walks on the network. In this paper, we study the spectra of walk matrix of the Koch network, which displays the prominent scale-free and small-world features. Utilizing the particular architecture of the network, we obtain all the eigenvalues and their corresponding multiplicities. Based on the link between the eigenvalues of walk matrix and random target access time defined as the expected time for a walker going from an arbitrary node to another one selected randomly according to the steady-state distribution, we then derive an explicit solution to the random target access time for random walks on the Koch network. Finally, we corroborate our computation for the eigenvalues by enumerating spanning trees in the Koch network, using the connection governing eigenvalues and spanning trees, where a spanning tree of a network is a subgraph of the network, that is, a tree containing all the nodes.

Theorising big IT programmes in healthcare: strong structuration theory meets actor-network theory.

PubMed

Greenhalgh, Trisha; Stones, Rob

2010-05-01

The UK National Health Service is grappling with various large and controversial IT programmes. We sought to develop a sharper theoretical perspective on the question "What happens - at macro-, meso- and micro-level - when government tries to modernise a health service with the help of big IT?" Using examples from data fragments at the micro-level of clinical work, we considered how structuration theory and actor-network theory (ANT) might be combined to inform empirical investigation. Giddens (1984) argued that social structures and human agency are recursively linked and co-evolve. ANT studies the relationships that link people and technologies in dynamic networks. It considers how discourses become inscribed in data structures and decision models of software, making certain network relations irreversible. Stones' (2005) strong structuration theory (SST) is a refinement of Giddens' work, systematically concerned with empirical research. It views human agents as linked in dynamic networks of position-practices. A quadripartite approcach considers [a] external social structures (conditions for action); [b] internal social structures (agents' capabilities and what they 'know' about the social world); [c] active agency and actions and [d] outcomes as they feed back on the position-practice network. In contrast to early structuration theory and ANT, SST insists on disciplined conceptual methodology and linking this with empirical evidence. In this paper, we adapt SST for the study of technology programmes, integrating elements from material interactionism and ANT. We argue, for example, that the position-practice network can be a socio-technical one in which technologies in conjunction with humans can be studied as 'actants'. Human agents, with their complex socio-cultural frames, are required to instantiate technology in social practices. Structurally relevant properties inscribed and embedded in technological artefacts constrain and enable human agency. The fortunes of healthcare IT programmes might be studied in terms of the interplay between these factors. Copyright 2010 Elsevier Ltd. All rights reserved.

Collective relaxation dynamics of small-world networks

NASA Astrophysics Data System (ADS)

Grabow, Carsten; Grosskinsky, Stefan; Kurths, Jürgen; Timme, Marc

2015-05-01

Complex networks exhibit a wide range of collective dynamic phenomena, including synchronization, diffusion, relaxation, and coordination processes. Their asymptotic dynamics is generically characterized by the local Jacobian, graph Laplacian, or a similar linear operator. The structure of networks with regular, small-world, and random connectivities are reasonably well understood, but their collective dynamical properties remain largely unknown. Here we present a two-stage mean-field theory to derive analytic expressions for network spectra. A single formula covers the spectrum from regular via small-world to strongly randomized topologies in Watts-Strogatz networks, explaining the simultaneous dependencies on network size N , average degree k , and topological randomness q . We present simplified analytic predictions for the second-largest and smallest eigenvalue, and numerical checks confirm our theoretical predictions for zero, small, and moderate topological randomness q , including the entire small-world regime. For large q of the order of one, we apply standard random matrix theory, thereby overarching the full range from regular to randomized network topologies. These results may contribute to our analytic and mechanistic understanding of collective relaxation phenomena of network dynamical systems.

Collective relaxation dynamics of small-world networks.

PubMed

Grabow, Carsten; Grosskinsky, Stefan; Kurths, Jürgen; Timme, Marc

2015-05-01

Complex networks exhibit a wide range of collective dynamic phenomena, including synchronization, diffusion, relaxation, and coordination processes. Their asymptotic dynamics is generically characterized by the local Jacobian, graph Laplacian, or a similar linear operator. The structure of networks with regular, small-world, and random connectivities are reasonably well understood, but their collective dynamical properties remain largely unknown. Here we present a two-stage mean-field theory to derive analytic expressions for network spectra. A single formula covers the spectrum from regular via small-world to strongly randomized topologies in Watts-Strogatz networks, explaining the simultaneous dependencies on network size N, average degree k, and topological randomness q. We present simplified analytic predictions for the second-largest and smallest eigenvalue, and numerical checks confirm our theoretical predictions for zero, small, and moderate topological randomness q, including the entire small-world regime. For large q of the order of one, we apply standard random matrix theory, thereby overarching the full range from regular to randomized network topologies. These results may contribute to our analytic and mechanistic understanding of collective relaxation phenomena of network dynamical systems.

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication

PubMed Central

Stetz, Gabrielle; Verkhivker, Gennady M.

2017-01-01

Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms. PMID:28095400

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication.

PubMed

Stetz, Gabrielle; Verkhivker, Gennady M

2017-01-01

Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms.

Stability and dynamical properties of material flow systems on random networks

NASA Astrophysics Data System (ADS)

Anand, K.; Galla, T.

2009-04-01

The theory of complex networks and of disordered systems is used to study the stability and dynamical properties of a simple model of material flow networks defined on random graphs. In particular we address instabilities that are characteristic of flow networks in economic, ecological and biological systems. Based on results from random matrix theory, we work out the phase diagram of such systems defined on extensively connected random graphs, and study in detail how the choice of control policies and the network structure affects stability. We also present results for more complex topologies of the underlying graph, focussing on finitely connected Erdös-Réyni graphs, Small-World Networks and Barabási-Albert scale-free networks. Results indicate that variability of input-output matrix elements, and random structures of the underlying graph tend to make the system less stable, while fast price dynamics or strong responsiveness to stock accumulation promote stability.

Multi-agent coordination in directed moving neighbourhood random networks

NASA Astrophysics Data System (ADS)

Shang, Yi-Lun

2010-07-01

This paper considers the consensus problem of dynamical multiple agents that communicate via a directed moving neighbourhood random network. Each agent performs random walk on a weighted directed network. Agents interact with each other through random unidirectional information flow when they coincide in the underlying network at a given instant. For such a framework, we present sufficient conditions for almost sure asymptotic consensus. Numerical examples are taken to show the effectiveness of the obtained results.

Random graph models of social networks.

PubMed

Newman, M E J; Watts, D J; Strogatz, S H

2002-02-19

We describe some new exactly solvable models of the structure of social networks, based on random graphs with arbitrary degree distributions. We give models both for simple unipartite networks, such as acquaintance networks, and bipartite networks, such as affiliation networks. We compare the predictions of our models to data for a number of real-world social networks and find that in some cases, the models are in remarkable agreement with the data, whereas in others the agreement is poorer, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph.

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.

Random matrix approach to plasmon resonances in the random impedance network model of disordered nanocomposites

NASA Astrophysics Data System (ADS)

Olekhno, N. A.; Beltukov, Y. M.

2018-05-01

Random impedance networks are widely used as a model to describe plasmon resonances in disordered metal-dielectric and other two-component nanocomposites. In the present work, the spectral properties of resonances in random networks are studied within the framework of the random matrix theory. We have shown that the appropriate ensemble of random matrices for the considered problem is the Jacobi ensemble (the MANOVA ensemble). The obtained analytical expressions for the density of states in such resonant networks show a good agreement with the results of numerical simulations in a wide range of metal filling fractions 0

The SH2 Domain–Containing Proteins in 21 Species Establish the Provenance and Scope of Phosphotyrosine Signaling in Eukaryotes

PubMed Central

Liu, Bernard A.; Shah, Eshana; Jablonowski, Karl; Stergachis, Andrew; Engelmann, Brett; Nash, Piers D.

2014-01-01

The Src homology 2 (SH2) domains are participants in metazoan signal transduction, acting as primary mediators for regulated protein-protein interactions with tyrosine-phosphorylated substrates. Here, we describe the origin and evolution of SH2 domain proteins by means of sequence analysis from 21 eukaryotic organisms from the basal unicellular eukaryotes, where SH2 domains first appeared, through the multicellular animals and increasingly complex metazoans. On the basis of our results, SH2 domains and phosphotyrosine signaling emerged in the early Unikonta, and the numbers of SH2 domains expanded in the choanoflagellate and metazoan lineages with the development of tyrosine kinases, leading to rapid elaboration of phosphotyrosine signaling in early multicellular animals. Our results also indicated that SH2 domains coevolved and the number of the domains expanded alongside protein tyrosine kinases and tyrosine phosphatases, thereby coupling phosphotyrosine signaling to downstream signaling networks. Gene duplication combined with domain gain or loss produced novel SH2-containing proteins that function within phosphotyrosine signaling, which likely have contributed to diversity and complexity in metazoans. We found that intra- and intermolecular interactions within and between SH2 domain proteins increased in prevalence along with organismal complexity and may function to generate more highly connected and robust phosphotyrosine signaling networks. PMID:22155787

Phylogenetic congruence between subtropical trees and their associated fungi.

PubMed

Liu, Xubing; Liang, Minxia; Etienne, Rampal S; Gilbert, Gregory S; Yu, Shixiao

2016-12-01

Recent studies have detected phylogenetic signals in pathogen-host networks for both soil-borne and leaf-infecting fungi, suggesting that pathogenic fungi may track or coevolve with their preferred hosts. However, a phylogenetically concordant relationship between multiple hosts and multiple fungi in has rarely been investigated. Using next-generation high-throughput DNA sequencing techniques, we analyzed fungal taxa associated with diseased leaves, rotten seeds, and infected seedlings of subtropical trees. We compared the topologies of the phylogenetic trees of the soil and foliar fungi based on the internal transcribed spacer (ITS) region with the phylogeny of host tree species based on matK , rbcL , atpB, and 5.8S genes. We identified 37 foliar and 103 soil pathogenic fungi belonging to the Ascomycota and Basidiomycota phyla and detected significantly nonrandom host-fungus combinations, which clustered on both the fungus phylogeny and the host phylogeny. The explicit evidence of congruent phylogenies between tree hosts and their potential fungal pathogens suggests either diffuse coevolution among the plant-fungal interaction networks or that the distribution of fungal species tracked spatially associated hosts with phylogenetically conserved traits and habitat preferences. Phylogenetic conservatism in plant-fungal interactions within a local community promotes host and parasite specificity, which is integral to the important role of fungi in promoting species coexistence and maintaining biodiversity of forest communities.

Assembly constraints drive co-evolution among ribosomal constituents.

PubMed

Mallik, Saurav; Akashi, Hiroshi; Kundu, Sudip

2015-06-23

Ribosome biogenesis, a central and essential cellular process, occurs through sequential association and mutual co-folding of protein-RNA constituents in a well-defined assembly pathway. Here, we construct a network of co-evolving nucleotide/amino acid residues within the ribosome and demonstrate that assembly constraints are strong predictors of co-evolutionary patterns. Predictors of co-evolution include a wide spectrum of structural reconstitution events, such as cooperativity phenomenon, protein-induced rRNA reconstitutions, molecular packing of different rRNA domains, protein-rRNA recognition, etc. A correlation between folding rate of small globular proteins and their topological features is known. We have introduced an analogous topological characteristic for co-evolutionary network of ribosome, which allows us to differentiate between rRNA regions subjected to rapid reconstitutions from those hindered by kinetic traps. Furthermore, co-evolutionary patterns provide a biological basis for deleterious mutation sites and further allow prediction of potential antibiotic targeting sites. Understanding assembly pathways of multicomponent macromolecules remains a key challenge in biophysics. Our study provides a 'proof of concept' that directly relates co-evolution to biophysical interactions during multicomponent assembly and suggests predictive power to identify candidates for critical functional interactions as well as for assembly-blocking antibiotic target sites. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Statistical physics of crime: a review.

PubMed

D'Orsogna, Maria R; Perc, Matjaž

2015-03-01

Containing the spread of crime in urban societies remains a major challenge. Empirical evidence suggests that, if left unchecked, crimes may be recurrent and proliferate. On the other hand, eradicating a culture of crime may be difficult, especially under extreme social circumstances that impair the creation of a shared sense of social responsibility. Although our understanding of the mechanisms that drive the emergence and diffusion of crime is still incomplete, recent research highlights applied mathematics and methods of statistical physics as valuable theoretical resources that may help us better understand criminal activity. We review different approaches aimed at modeling and improving our understanding of crime, focusing on the nucleation of crime hotspots using partial differential equations, self-exciting point process and agent-based modeling, adversarial evolutionary games, and the network science behind the formation of gangs and large-scale organized crime. We emphasize that statistical physics of crime can relevantly inform the design of successful crime prevention strategies, as well as improve the accuracy of expectations about how different policing interventions should impact malicious human activity that deviates from social norms. We also outline possible directions for future research, related to the effects of social and coevolving networks and to the hierarchical growth of criminal structures due to self-organization. Copyright © 2014 Elsevier B.V. All rights reserved.

The subtle body: an interoceptive map of central nervous system function and meditative mind-brain-body integration.

PubMed

Loizzo, Joseph J

2016-06-01

Meditation research has begun to clarify the brain effects and mechanisms of contemplative practices while generating a range of typologies and explanatory models to guide further study. This comparative review explores a neglected area relevant to current research: the validity of a traditional central nervous system (CNS) model that coevolved with the practices most studied today and that provides the first comprehensive neural-based typology and mechanistic framework of contemplative practices. The subtle body model, popularly known as the chakra system from Indian yoga, was and is used as a map of CNS function in traditional Indian and Tibetan medicine, neuropsychiatry, and neuropsychology. The study presented here, based on the Nalanda tradition, shows that the subtle body model can be cross-referenced with modern CNS maps and challenges modern brain maps with its embodied network model of CNS function. It also challenges meditation research by: (1) presenting a more rigorous, neural-based typology of contemplative practices; (2) offering a more refined and complete network model of the mechanisms of contemplative practices; and (3) serving as an embodied, interoceptive neurofeedback aid that is more user friendly and complete than current teaching aids for clinical and practical applications of contemplative practice. © 2016 New York Academy of Sciences.

The Effect of Parasitism and Interpopulation Hybridization on Aedes albopictus (Diptera: Culicidae) Fitness.

PubMed

Tseng, Michelle

2017-09-01

Recent research in mosquito population genetics suggests that interpopulation hybridization has likely contributed to the rapid spread of the container-breeding mosquitoes. Here, I used laboratory experiments to investigate whether interpopulation Aedes (Stegomyia) albopictus (Skuse) F1 and F2 hybrids exhibit higher fitness than parental populations, and whether hybrid mosquito performance is related to infection by the coevolved protozoan parasite Ascogregarina taiwanensis (Lien and Levine). Overall, there were significant differences in development time, wing length, and survival between the two parental mosquito populations, but no difference in per capita growth rate r. Hybrid mosquitoes were generally intermediate in phenotype to the parentals, except that F2 females were significantly larger than the midparent average. In addition, As. taiwanensis parasites produced fewest oocysts when they were reared in hosts of hybrid origin. These data suggest that hybridization between previously isolated mosquito populations can result in slight increases in potential mosquito reproductive success, via increased hybrid body size, and via the temporary escape from coevolved parasites. These findings are significant because studies have shown that even slight hybrid vigor can have positive fitness consequences for population persistence. Although this was a laboratory experiment extending only to the F2 generation, many other invasive insects also carry coevolved parasites, and thus the patterns seen in this mosquito system may be broadly relevant. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Parasite transmission among relatives halts Red Queen dynamics.

PubMed

Greenspoon, Philip B; Mideo, Nicole

2017-03-01

The theory that coevolving hosts and parasites create a fluctuating selective environment for one another (i.e., produce Red Queen dynamics) has deep roots in evolutionary biology; yet empirical evidence for Red Queen dynamics remains scarce. Fluctuating coevolutionary dynamics underpin the Red Queen hypothesis for the evolution of sex, as well as hypotheses explaining the persistence of genetic variation under sexual selection, local parasite adaptation, the evolution of mutation rate, and the evolution of nonrandom mating. Coevolutionary models that exhibit Red Queen dynamics typically assume that hosts and parasites encounter one another randomly. However, if related individuals aggregate into family groups or are clustered spatially, related hosts will be more likely to encounter parasites transmitted by genetically similar individuals. Using a model that incorporates familial parasite transmission, we show that a slight degree of familial parasite transmission is sufficient to halt coevolutionary fluctuations. Our results predict that evidence for Red Queen dynamics, and its evolutionary consequences, are most likely to be found in biological systems in which hosts and parasites mix mainly at random, and are less likely to be found in systems with familial aggregation. This presents a challenge to the Red Queen hypothesis and other hypotheses that depend on coevolutionary cycling. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

New scaling relation for information transfer in biological networks

PubMed Central

Kim, Hyunju; Davies, Paul; Walker, Sara Imari

2015-01-01

We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781–4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös–Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties. PMID:26701883

Effect of self-organized interdependence between populations on the evolution of cooperation

NASA Astrophysics Data System (ADS)

Luo, Chao; Zhang, Xiaolin

2017-01-01

In this article, based on interdependent networks, the effect of self-organized interdependence on the evolution of cooperation is studied. Different from the previous works, the interdependent strength, which can effectively improve the fitness of players, is taken as a kind of limited resources and co-evolves with players' strategy. We show that the self-organization of interdependent strength would spontaneously lead to power law distribution at the stationary state, where the level of cooperation in system can be significantly promoted. Furthermore, when intermediate quantity of interdependence resources existing in system, the power law distribution is most evident with the power β ≈ 1.72, meanwhile the level of cooperation also reaches the maximum value. We discuss the related microscopic system properties which are responsible for the observed results and also demonstrate that the power law distribution of interdependence resources is an elementary property which is robust against the governing repeated games and the initial resources allocation patterns.

Deconstruction of the Ras switching cycle through saturation mutagenesis

PubMed Central

Bandaru, Pradeep; Shah, Neel H; Bhattacharyya, Moitrayee; Barton, John P; Kondo, Yasushi; Cofsky, Joshua C; Gee, Christine L; Chakraborty, Arup K; Kortemme, Tanja; Ranganathan, Rama; Kuriyan, John

2017-01-01

Ras proteins are highly conserved signaling molecules that exhibit regulated, nucleotide-dependent switching between active and inactive states. The high conservation of Ras requires mechanistic explanation, especially given the general mutational tolerance of proteins. Here, we use deep mutational scanning, biochemical analysis and molecular simulations to understand constraints on Ras sequence. Ras exhibits global sensitivity to mutation when regulated by a GTPase activating protein and a nucleotide exchange factor. Removing the regulators shifts the distribution of mutational effects to be largely neutral, and reveals hotspots of activating mutations in residues that restrain Ras dynamics and promote the inactive state. Evolutionary analysis, combined with structural and mutational data, argue that Ras has co-evolved with its regulators in the vertebrate lineage. Overall, our results show that sequence conservation in Ras depends strongly on the biochemical network in which it operates, providing a framework for understanding the origin of global selection pressures on proteins. DOI: http://dx.doi.org/10.7554/eLife.27810.001 PMID:28686159

Origins of Allostery and Evolvability in Proteins: A Case Study.

PubMed

Raman, Arjun S; White, K Ian; Ranganathan, Rama

2016-07-14

Proteins display the capacity for adaptation to new functions, a property critical for evolvability. But what structural principles underlie the capacity for adaptation? Here, we show that adaptation to a physiologically distinct class of ligand specificity in a PSD95, DLG1, ZO-1 (PDZ) domain preferentially occurs through class-bridging intermediate mutations located distant from the ligand-binding site. These mutations provide a functional link between ligand classes and demonstrate the principle of "conditional neutrality" in mediating evolutionary adaptation. Structures show that class-bridging mutations work allosterically to open up conformational plasticity at the active site, permitting novel functions while retaining existing function. More generally, the class-bridging phenotype arises from mutations in an evolutionarily conserved network of coevolving amino acids in the PDZ family (the sector) that connects the active site to distant surface sites. These findings introduce the concept that allostery in proteins could have its origins not in protein function but in the capacity to adapt. Copyright © 2016 Elsevier Inc. All rights reserved.

Hodge Decomposition of Information Flow on Small-World Networks.

PubMed

Haruna, Taichi; Fujiki, Yuuya

2016-01-01

We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory, we decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows. The harmonic and curl flows represent globally circular and locally circular components, respectively. The Watts-Strogatz model bridges the two extreme network topologies, a lattice network and a random network, by a single parameter that is the probability of random rewiring. The small-world topology is realized within a certain range between them. By numerical simulation we found that as networks become more random the ratio of harmonic flow to the total magnitude of information flow increases whereas the ratio of curl flow decreases. Furthermore, both quantities are significantly enhanced from the level when only network structure is considered for the network close to a random network and a lattice network, respectively. Finally, the sum of these two ratios takes its maximum value within the small-world region. These findings suggest that the dynamical information counterpart of global integration and that of local segregation are the harmonic flow and the curl flow, respectively, and that a part of the small-world region is dominated by internal circulation of information flow.

Creating, generating and comparing random network models with NetworkRandomizer.

PubMed

Tosadori, Gabriele; Bestvina, Ivan; Spoto, Fausto; Laudanna, Carlo; Scardoni, Giovanni

2016-01-01

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

Systemic risk on different interbank network topologies

NASA Astrophysics Data System (ADS)

Lenzu, Simone; Tedeschi, Gabriele

2012-09-01

In this paper we develop an interbank market with heterogeneous financial institutions that enter into lending agreements on different network structures. Credit relationships (links) evolve endogenously via a fitness mechanism based on agents' performance. By changing the agent's trust on its neighbor's performance, interbank linkages self-organize themselves into very different network architectures, ranging from random to scale-free topologies. We study which network architecture can make the financial system more resilient to random attacks and how systemic risk spreads over the network. To perturb the system, we generate a random attack via a liquidity shock. The hit bank is not automatically eliminated, but its failure is endogenously driven by its incapacity to raise liquidity in the interbank network. Our analysis shows that a random financial network can be more resilient than a scale free one in case of agents' heterogeneity.

Coevolutionary Free Lunches

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Macready, William G.

2005-01-01

Recent work on the mathematical foundations of optimization has begun to uncover its rich structure. In particular, the "No Free Lunch" (NFL) theorems state that any two algorithms are equivalent when their performance is averaged across all possible problems. This highlights the need for exploiting problem-specific knowledge to achieve better than random performance. In this paper we present a general framework covering more search scenarios. In addition to the optimization scenarios addressed in the NFL results, this framework covers multi-armed bandit problems and evolution of multiple co-evolving players. As a particular instance of the latter, it covers "self-play" problems. In these problems the set of players work together to produce a champion, who then engages one or more antagonists in a subsequent multi-player game. In contrast to the traditional optimization case where the NFL results hold, we show that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems. We consider the implications of these results to biology where there is no champion.

Power law tails in phylogenetic systems.

PubMed

Qin, Chongli; Colwell, Lucy J

2018-01-23

Covariance analysis of protein sequence alignments uses coevolving pairs of sequence positions to predict features of protein structure and function. However, current methods ignore the phylogenetic relationships between sequences, potentially corrupting the identification of covarying positions. Here, we use random matrix theory to demonstrate the existence of a power law tail that distinguishes the spectrum of covariance caused by phylogeny from that caused by structural interactions. The power law is essentially independent of the phylogenetic tree topology, depending on just two parameters-the sequence length and the average branch length. We demonstrate that these power law tails are ubiquitous in the large protein sequence alignments used to predict contacts in 3D structure, as predicted by our theory. This suggests that to decouple phylogenetic effects from the interactions between sequence distal sites that control biological function, it is necessary to remove or down-weight the eigenvectors of the covariance matrix with largest eigenvalues. We confirm that truncating these eigenvectors improves contact prediction.

Effects of topology on network evolution

NASA Astrophysics Data System (ADS)

Oikonomou, Panos; Cluzel, Philippe

2006-08-01

The ubiquity of scale-free topology in nature raises the question of whether this particular network design confers an evolutionary advantage. A series of studies has identified key principles controlling the growth and the dynamics of scale-free networks. Here, we use neuron-based networks of boolean components as a framework for modelling a large class of dynamical behaviours in both natural and artificial systems. Applying a training algorithm, we characterize how networks with distinct topologies evolve towards a pre-established target function through a process of random mutations and selection. We find that homogeneous random networks and scale-free networks exhibit drastically different evolutionary paths. Whereas homogeneous random networks accumulate neutral mutations and evolve by sparse punctuated steps, scale-free networks evolve rapidly and continuously. Remarkably, this latter property is robust to variations of the degree exponent. In contrast, homogeneous random networks require a specific tuning of their connectivity to optimize their ability to evolve. These results highlight an organizing principle that governs the evolution of complex networks and that can improve the design of engineered systems.

Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach.

DTIC Science & Technology

1998-05-01

Coverage Probability with a Random Optimization Procedure: An Artificial Neural Network Approach by Biing T. Guan, George Z. Gertner, and Alan B...Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach 6. AUTHOR(S) Biing...coverage based on past coverage. Approach A literature survey was conducted to identify artificial neural network analysis techniques applicable for

Distributed Detection with Collisions in a Random, Single-Hop Wireless Sensor Network

DTIC Science & Technology

2013-05-26

public release; distribution is unlimited. Distributed detection with collisions in a random, single-hop wireless sensor network The views, opinions...1274 2 ABSTRACT Distributed detection with collisions in a random, single-hop wireless sensor network Report Title We consider the problem of... WIRELESS SENSOR NETWORK Gene T. Whipps?† Emre Ertin† Randolph L. Moses† ?U.S. Army Research Laboratory, Adelphi, MD 20783 †The Ohio State University

Disentangling dynamic networks: Separated and joint expressions of functional connectivity patterns in time.

PubMed

Leonardi, Nora; Shirer, William R; Greicius, Michael D; Van De Ville, Dimitri

2014-12-01

Resting-state functional connectivity (FC) is highly variable across the duration of a scan. Groups of coevolving connections, or reproducible patterns of dynamic FC (dFC), have been revealed in fluctuating FC by applying unsupervised learning techniques. Based on results from k-means clustering and sliding-window correlations, it has recently been hypothesized that dFC may cycle through several discrete FC states. Alternatively, it has been proposed to represent dFC as a linear combination of multiple FC patterns using principal component analysis. As it is unclear whether sparse or nonsparse combinations of FC patterns are most appropriate, and as this affects their interpretation and use as markers of cognitive processing, the goal of our study was to evaluate the impact of sparsity by performing an empirical evaluation of simulated, task-based, and resting-state dFC. To this aim, we applied matrix factorizations subject to variable constraints in the temporal domain and studied both the reproducibility of ensuing representations of dFC and the expression of FC patterns over time. During subject-driven tasks, dFC was well described by alternating FC states in accordance with the nature of the data. The estimated FC patterns showed a rich structure with combinations of known functional networks enabling accurate identification of three different tasks. During rest, dFC was better described by multiple FC patterns that overlap. The executive control networks, which are critical for working memory, appeared grouped alternately with externally or internally oriented networks. These results suggest that combinations of FC patterns can provide a meaningful way to disentangle resting-state dFC. © 2014 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.

Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2016-10-06

Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

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

NASA Astrophysics Data System (ADS)

Ward, Jonathan A.; Grindrod, Peter

2014-07-01

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

Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia.

PubMed

Lo, Chun-Yi Zac; Su, Tsung-Wei; Huang, Chu-Chung; Hung, Chia-Chun; Chen, Wei-Ling; Lan, Tsuo-Hung; Lin, Ching-Po; Bullmore, Edward T

2015-07-21

Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients' networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population.

Mate-choice copying: A fitness-enhancing behavior that evolves by indirect selection.

PubMed

Santos, Mauro; Sapage, Manuel; Matos, Margarida; Varela, Susana A M

2017-06-01

A spatially explicit, individual-based simulation model is used to study the spread of an allele for mate-choice copying (MCC) through horizontal cultural transmission when female innate preferences do or do not coevolve with a male viability-increasing trait. Evolution of MCC is unlikely when innate female preferences coevolve with the trait, as copier females cannot express a higher preference than noncopier females for high-fitness males. However, if a genetic polymorphism for innate preference persists in the population, MCC can evolve by indirect selection through hitchhiking: the copying allele hitchhikes on the male trait. MCC can be an adaptive behavior-that is, a behavior that increases a population's average fitness relative to populations without MCC-even though the copying allele itself may be neutral or mildly deleterious. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

Eco-evolutionary dynamics in a coevolving host-virus system.

PubMed

Frickel, Jens; Sieber, Michael; Becks, Lutz

2016-04-01

Eco-evolutionary dynamics have been shown to be important for understanding population and community stability and their adaptive potential. However, coevolution in the framework of eco-evolutionary theory has not been addressed directly. Combining experiments with an algal host and its viral parasite, and mathematical model analyses we show eco-evolutionary dynamics in antagonistic coevolving populations. The interaction between antagonists initially resulted in arms race dynamics (ARD) with selective sweeps, causing oscillating host-virus population dynamics. However, ARD ended and populations stabilised after the evolution of a general resistant host, whereas a trade-off between host resistance and growth then maintained host diversity over time (trade-off driven dynamics). Most importantly, our study shows that the interaction between ecology and evolution had important consequences for the predictability of the mode and tempo of adaptive change and for the stability and adaptive potential of populations. © 2016 John Wiley & Sons Ltd/CNRS.

Percolation of localized attack on complex networks

NASA Astrophysics Data System (ADS)

Shao, Shuai; Huang, Xuqing; Stanley, H. Eugene; Havlin, Shlomo

2015-02-01

The robustness of complex networks against node failure and malicious attack has been of interest for decades, while most of the research has focused on random attack or hub-targeted attack. In many real-world scenarios, however, attacks are neither random nor hub-targeted, but localized, where a group of neighboring nodes in a network are attacked and fail. In this paper we develop a percolation framework to analytically and numerically study the robustness of complex networks against such localized attack. In particular, we investigate this robustness in Erdős-Rényi networks, random-regular networks, and scale-free networks. Our results provide insight into how to better protect networks, enhance cybersecurity, and facilitate the design of more robust infrastructures.

The informational architecture of the cell.

PubMed

Walker, Sara Imari; Kim, Hyunju; Davies, Paul C W

2016-03-13

We compare the informational architecture of biological and random networks to identify informational features that may distinguish biological networks from random. The study presented here focuses on the Boolean network model for regulation of the cell cycle of the fission yeast Schizosaccharomyces pombe. We compare calculated values of local and global information measures for the fission yeast cell cycle to the same measures as applied to two different classes of random networks: Erdös-Rényi and scale-free. We report patterns in local information processing and storage that do indeed distinguish biological from random, associated with control nodes that regulate the function of the fission yeast cell-cycle network. Conversely, we find that integrated information, which serves as a global measure of 'emergent' information processing, does not differ from random for the case presented. We discuss implications for our understanding of the informational architecture of the fission yeast cell-cycle network in particular, and more generally for illuminating any distinctive physics that may be operative in life. © 2016 The Author(s).

Simulation of the mechanical behavior of random fiber networks with different microstructure.

PubMed

Hatami-Marbini, H

2018-05-24

Filamentous protein networks are broadly encountered in biological systems such as cytoskeleton and extracellular matrix. Many numerical studies have been conducted to better understand the fundamental mechanisms behind the striking mechanical properties of these networks. In most of these previous numerical models, the Mikado algorithm has been used to represent the network microstructure. Here, a different algorithm is used to create random fiber networks in order to investigate possible roles of architecture on the elastic behavior of filamentous networks. In particular, random fibrous structures are generated from the growth of individual fibers from random nucleation points. We use computer simulations to determine the mechanical behavior of these networks in terms of their model parameters. The findings are presented and discussed along with the response of Mikado fiber networks. We demonstrate that these alternative networks and Mikado networks show a qualitatively similar response. Nevertheless, the overall elasticity of Mikado networks is stiffer compared to that of the networks created using the alternative algorithm. We describe the effective elasticity of both network types as a function of their line density and of the material properties of the filaments. We also characterize the ratio of bending and axial energy and discuss the behavior of these networks in terms of their fiber density distribution and coordination number.

Infectious disease control using contact tracing in random and scale-free networks

PubMed Central

Kiss, Istvan Z; Green, Darren M; Kao, Rowland R

2005-01-01

Contact tracing aims to identify and isolate individuals that have been in contact with infectious individuals. The efficacy of contact tracing and the hierarchy of traced nodes—nodes with higher degree traced first—is investigated and compared on random and scale-free (SF) networks with the same number of nodes N and average connection K. For values of the transmission rate larger than a threshold, the final epidemic size on SF networks is smaller than that on corresponding random networks. While in random networks new infectious and traced nodes from all classes have similar average degrees, in SF networks the average degree of nodes that are in more advanced stages of the disease is higher at any given time. On SF networks tracing removes possible sources of infection with high average degree. However a higher tracing effort is required to control the epidemic than on corresponding random networks due to the high initial velocity of spread towards the highly connected nodes. An increased latency period fails to significantly improve contact tracing efficacy. Contact tracing has a limited effect if the removal rate of susceptible nodes is relatively high, due to the fast local depletion of susceptible nodes. PMID:16849217

Efficient sampling of complex network with modified random walk strategies

NASA Astrophysics Data System (ADS)

Xie, Yunya; Chang, Shuhua; Zhang, Zhipeng; Zhang, Mi; Yang, Lei

2018-02-01

We present two novel random walk strategies, choosing seed node (CSN) random walk and no-retracing (NR) random walk. Different from the classical random walk sampling, the CSN and NR strategies focus on the influences of the seed node choice and path overlap, respectively. Three random walk samplings are applied in the Erdös-Rényi (ER), Barabási-Albert (BA), Watts-Strogatz (WS), and the weighted USAir networks, respectively. Then, the major properties of sampled subnets, such as sampling efficiency, degree distributions, average degree and average clustering coefficient, are studied. The similar conclusions can be reached with these three random walk strategies. Firstly, the networks with small scales and simple structures are conducive to the sampling. Secondly, the average degree and the average clustering coefficient of the sampled subnet tend to the corresponding values of original networks with limited steps. And thirdly, all the degree distributions of the subnets are slightly biased to the high degree side. However, the NR strategy performs better for the average clustering coefficient of the subnet. In the real weighted USAir networks, some obvious characters like the larger clustering coefficient and the fluctuation of degree distribution are reproduced well by these random walk strategies.

Stochastic Models of Emerging Infectious Disease Transmission on Adaptive Random Networks

PubMed Central

Pipatsart, Navavat; Triampo, Wannapong

2017-01-01

We presented adaptive random network models to describe human behavioral change during epidemics and performed stochastic simulations of SIR (susceptible-infectious-recovered) epidemic models on adaptive random networks. The interplay between infectious disease dynamics and network adaptation dynamics was investigated in regard to the disease transmission and the cumulative number of infection cases. We found that the cumulative case was reduced and associated with an increasing network adaptation probability but was increased with an increasing disease transmission probability. It was found that the topological changes of the adaptive random networks were able to reduce the cumulative number of infections and also to delay the epidemic peak. Our results also suggest the existence of a critical value for the ratio of disease transmission and adaptation probabilities below which the epidemic cannot occur. PMID:29075314

Network meta-analysis of disconnected networks: How dangerous are random baseline treatment effects?

PubMed

Béliveau, Audrey; Goring, Sarah; Platt, Robert W; Gustafson, Paul

2017-12-01

In network meta-analysis, the use of fixed baseline treatment effects (a priori independent) in a contrast-based approach is regularly preferred to the use of random baseline treatment effects (a priori dependent). That is because, often, there is not a need to model baseline treatment effects, which carry the risk of model misspecification. However, in disconnected networks, fixed baseline treatment effects do not work (unless extra assumptions are made), as there is not enough information in the data to update the prior distribution on the contrasts between disconnected treatments. In this paper, we investigate to what extent the use of random baseline treatment effects is dangerous in disconnected networks. We take 2 publicly available datasets of connected networks and disconnect them in multiple ways. We then compare the results of treatment comparisons obtained from a Bayesian contrast-based analysis of each disconnected network using random normally distributed and exchangeable baseline treatment effects to those obtained from a Bayesian contrast-based analysis of their initial connected network using fixed baseline treatment effects. For the 2 datasets considered, we found that the use of random baseline treatment effects in disconnected networks was appropriate. Because those datasets were not cherry-picked, there should be other disconnected networks that would benefit from being analyzed using random baseline treatment effects. However, there is also a risk for the normality and exchangeability assumption to be inappropriate in other datasets even though we have not observed this situation in our case study. We provide code, so other datasets can be investigated. Copyright © 2017 John Wiley & Sons, Ltd.

P450 AND METABOLISM IN TOXICOLOGY

EPA Science Inventory

The cytochromes P450 catalyze the initial phase of detoxification of many environmental chemicals, xenobiotic, drugs and the secondary metabolic product of plants. Plant secondary chemicals can be highly toxic, and they evolved in a coevolving plant - animal warfare - the plants ...

Antiviral immunity and virus vaccines

USDA-ARS?s Scientific Manuscript database

As obligate intracellular organisms, viruses have co-evolved with their respective host species, which in turn have evolved diverse and sophisticated capabilities to protect themselves against viral infections and their associated diseases. Viruses have also evolved a remarkable variety of strategie...

Quasirandom geometric networks from low-discrepancy sequences

NASA Astrophysics Data System (ADS)

Estrada, Ernesto

2017-08-01

We define quasirandom geometric networks using low-discrepancy sequences, such as Halton, Sobol, and Niederreiter. The networks are built in d dimensions by considering the d -tuples of digits generated by these sequences as the coordinates of the vertices of the networks in a d -dimensional Id unit hypercube. Then, two vertices are connected by an edge if they are at a distance smaller than a connection radius. We investigate computationally 11 network-theoretic properties of two-dimensional quasirandom networks and compare them with analogous random geometric networks. We also study their degree distribution and their spectral density distributions. We conclude from this intensive computational study that in terms of the uniformity of the distribution of the vertices in the unit square, the quasirandom networks look more random than the random geometric networks. We include an analysis of potential strategies for generating higher-dimensional quasirandom networks, where it is know that some of the low-discrepancy sequences are highly correlated. In this respect, we conclude that up to dimension 20, the use of scrambling, skipping and leaping strategies generate quasirandom networks with the desired properties of uniformity. Finally, we consider a diffusive process taking place on the nodes and edges of the quasirandom and random geometric graphs. We show that the diffusion time is shorter in the quasirandom graphs as a consequence of their larger structural homogeneity. In the random geometric graphs the diffusion produces clusters of concentration that make the process more slow. Such clusters are a direct consequence of the heterogeneous and irregular distribution of the nodes in the unit square in which the generation of random geometric graphs is based on.

Noisy scale-free networks

NASA Astrophysics Data System (ADS)

Scholz, Jan; Dejori, Mathäus; Stetter, Martin; Greiner, Martin

2005-05-01

The impact of observational noise on the analysis of scale-free networks is studied. Various noise sources are modeled as random link removal, random link exchange and random link addition. Emphasis is on the resulting modifications for the node-degree distribution and for a functional ranking based on betweenness centrality. The implications for estimated gene-expressed networks for childhood acute lymphoblastic leukemia are discussed.

Entropy of spatial network ensembles

NASA Astrophysics Data System (ADS)

Coon, Justin P.; Dettmann, Carl P.; Georgiou, Orestis

2018-04-01

We analyze complexity in spatial network ensembles through the lens of graph entropy. Mathematically, we model a spatial network as a soft random geometric graph, i.e., a graph with two sources of randomness, namely nodes located randomly in space and links formed independently between pairs of nodes with probability given by a specified function (the "pair connection function") of their mutual distance. We consider the general case where randomness arises in node positions as well as pairwise connections (i.e., for a given pair distance, the corresponding edge state is a random variable). Classical random geometric graph and exponential graph models can be recovered in certain limits. We derive a simple bound for the entropy of a spatial network ensemble and calculate the conditional entropy of an ensemble given the node location distribution for hard and soft (probabilistic) pair connection functions. Under this formalism, we derive the connection function that yields maximum entropy under general constraints. Finally, we apply our analytical framework to study two practical examples: ad hoc wireless networks and the US flight network. Through the study of these examples, we illustrate that both exhibit properties that are indicative of nearly maximally entropic ensembles.

Synchronizability of random rectangular graphs

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

Estrada, Ernesto, E-mail: ernesto.estrada@strath.ac.uk; Chen, Guanrong

2015-08-15

Random rectangular graphs (RRGs) represent a generalization of the random geometric graphs in which the nodes are embedded into hyperrectangles instead of on hypercubes. The synchronizability of RRG model is studied. Both upper and lower bounds of the eigenratio of the network Laplacian matrix are determined analytically. It is proven that as the rectangular network is more elongated, the network becomes harder to synchronize. The synchronization processing behavior of a RRG network of chaotic Lorenz system nodes is numerically investigated, showing complete consistence with the theoretical results.

Toward negative Poisson's ratio composites: Investigation of the auxetic behavior of fibrous networks

NASA Astrophysics Data System (ADS)

Tatlier, Mehmet Seha

Random fibrous can be found among natural and synthetic materials. Some of these random fibrous networks possess negative Poisson's ratio and they are extensively called auxetic materials. The governing mechanisms behind this counter intuitive property in random networks are yet to be understood and this kind of auxetic material remains widely under-explored. However, most of synthetic auxetic materials suffer from their low strength. This shortcoming can be rectified by developing high strength auxetic composites. The process of embedding auxetic random fibrous networks in a polymer matrix is an attractive alternate route to the manufacture of auxetic composites, however before such an approach can be developed, a methodology for designing fibrous networks with the desired negative Poisson's ratios must first be established. This requires an understanding of the factors which bring about negative Poisson's ratios in these materials. In this study, a numerical model is presented in order to investigate the auxetic behavior in compressed random fiber networks. Finite element analyses of three-dimensional stochastic fiber networks were performed to gain insight into the effects of parameters such as network anisotropy, network density, and degree of network compression on the out-of-plane Poisson's ratio and Young's modulus. The simulation results suggest that the compression is the critical parameter that gives rise to negative Poisson's ratio while anisotropy significantly promotes the auxetic behavior. This model can be utilized to design fibrous auxetic materials and to evaluate feasibility of developing auxetic composites by using auxetic fibrous networks as the reinforcing layer.

A Dynamic Model of Adolescent Friendship Networks, Parental Influences, and Smoking.

PubMed

Lakon, Cynthia M; Wang, Cheng; Butts, Carter T; Jose, Rupa; Timberlake, David S; Hipp, John R

2015-09-01

Peer and parental influences are critical socializing forces shaping adolescent development, including the co-evolving processes of friendship tie choice and adolescent smoking. This study examines aspects of adolescent friendship networks and dimensions of parental influences shaping friendship tie choice and smoking, including parental support, parental monitoring, and the parental home smoking environment using a Stochastic Actor-Based model. With data from three waves of the National Longitudinal Study of Adolescent Health of youth in grades 7 through 12, including the In-School Survey, the first wave of the In-Home survey occurring 6 months later, and the second wave of the In-Home survey, occurring one year later, this study utilizes two samples based on the social network data collected in the longitudinal saturated sample of sixteen schools. One consists of twelve small schools (n = 1,284, 50.93 % female), and the other of one large school (n = 976, 48.46 % female). The findings indicated that reciprocity, choosing a friend of a friend as a friend, and smoking similarity increased friendship tie choice behavior, as did parental support. Parental monitoring interacted with choosing friends who smoke in affecting friendship tie choice, as at higher levels of parental monitoring, youth chose fewer friends that smoked. A parental home smoking context conducive to smoking decreased the number of friends adolescents chose. Peer influence and a parental home smoking environment conducive to smoking increased smoking, while parental monitoring decreased it in the large school. Overall, peer and parental factors affected the coevolution of friendship tie choice and smoking, directly and multiplicatively.

A Dynamic Model of Adolescent Friendship Networks, Parental Influences, and Smoking

PubMed Central

Wang, Cheng; Butts, Carter T.; Jose, Rupa; Timberlake, David S.; Hipp, John R.

2015-01-01

Peer and parental influences are critical socializing forces shaping adolescent development, including the co-evolving processes of friendship tie choice and adolescent smoking. This study examines aspects of adolescent friendship networks and dimensions of parental influences shaping friendship tie choice and smoking, including parental support, parental monitoring, and the parental home smoking environment using a Stochastic Actor-Based model. With data from three waves of the National Longitudinal Study of Adolescent Health of youth in grades 7 through 12, including the In-School Survey, the first wave of the In-Home survey occurring 6 months later, and the second wave of the In-Home survey, occurring one year later, this study utilizes two samples based on the social network data collected in the longitudinal saturated sample of sixteen schools. One consists of twelve small schools (n = 1,284, 50.93 % female), and the other of one large school (n = 976, 48.46 % female). The findings indicated that reciprocity, choosing a friend of a friend as a friend, and smoking similarity increased friendship tie choice behavior, as did parental support. Parental monitoring interacted with choosing friends who smoke in affecting friendship tie choice, as at higher levels of parental monitoring, youth chose fewer friends that smoked. A parental home smoking context conducive to smoking decreased the number of friends adolescents chose. Peer influence and a parental home smoking environment conducive to smoking increased smoking, while parental monitoring decreased it in the large school. Overall, peer and parental factors affected the coevolution of friendship tie choice and smoking, directly and multiplicatively. PMID:25239115

Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks

PubMed Central

Hosseini, S. M. Hadi; Kesler, Shelli R.

2013-01-01

In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures. PMID:23840672

Immunization of Epidemics in Multiplex Networks

PubMed Central

Zhao, Dawei; Wang, Lianhai; Li, Shudong; Wang, Zhen; Wang, Lin; Gao, Bo

2014-01-01

Up to now, immunization of disease propagation has attracted great attention in both theoretical and experimental researches. However, vast majority of existing achievements are limited to the simple assumption of single layer networked population, which seems obviously inconsistent with recent development of complex network theory: each node could possess multiple roles in different topology connections. Inspired by this fact, we here propose the immunization strategies on multiplex networks, including multiplex node-based random (targeted) immunization and layer node-based random (targeted) immunization. With the theory of generating function, theoretical analysis is developed to calculate the immunization threshold, which is regarded as the most critical index for the effectiveness of addressed immunization strategies. Interestingly, both types of random immunization strategies show more efficiency in controlling disease spreading on multiplex Erdös-Rényi (ER) random networks; while targeted immunization strategies provide better protection on multiplex scale-free (SF) networks. PMID:25401755

Immunization of epidemics in multiplex networks.

PubMed

Zhao, Dawei; Wang, Lianhai; Li, Shudong; Wang, Zhen; Wang, Lin; Gao, Bo

2014-01-01

Up to now, immunization of disease propagation has attracted great attention in both theoretical and experimental researches. However, vast majority of existing achievements are limited to the simple assumption of single layer networked population, which seems obviously inconsistent with recent development of complex network theory: each node could possess multiple roles in different topology connections. Inspired by this fact, we here propose the immunization strategies on multiplex networks, including multiplex node-based random (targeted) immunization and layer node-based random (targeted) immunization. With the theory of generating function, theoretical analysis is developed to calculate the immunization threshold, which is regarded as the most critical index for the effectiveness of addressed immunization strategies. Interestingly, both types of random immunization strategies show more efficiency in controlling disease spreading on multiplex Erdös-Rényi (ER) random networks; while targeted immunization strategies provide better protection on multiplex scale-free (SF) networks.

A Statistical Method to Distinguish Functional Brain Networks

PubMed Central

Fujita, André; Vidal, Maciel C.; Takahashi, Daniel Y.

2017-01-01

One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism (p < 0.001). PMID:28261045

A Statistical Method to Distinguish Functional Brain Networks.

PubMed

Fujita, André; Vidal, Maciel C; Takahashi, Daniel Y

2017-01-01

One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism ( p < 0.001).

Impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach.

PubMed

Chandrasekar, A; Rakkiyappan, R; Cao, Jinde

2015-10-01

This paper studies the impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach. The array of neural networks are coupled in a random fashion which is governed by Bernoulli random variable. The aim of this paper is to obtain the synchronization criteria, which is suitable for both exactly known and partly unknown transition probabilities such that the coupled neural network is synchronized with mixed time-delay. The considered impulsive effects can be synchronized at partly unknown transition probabilities. Besides, a multiple integral approach is also proposed to strengthen the Markovian jumping randomly coupled neural networks with partly unknown transition probabilities. By making use of Kronecker product and some useful integral inequalities, a novel Lyapunov-Krasovskii functional was designed for handling the coupled neural network with mixed delay and then impulsive synchronization criteria are solvable in a set of linear matrix inequalities. Finally, numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

PLANNING AND RESPONSE IN THE AFTERMATH OF A LARGE CRISIS: AN AGENT-BASED INFORMATICS FRAMEWORK*

PubMed Central

Barrett, Christopher; Bisset, Keith; Chandan, Shridhar; Chen, Jiangzhuo; Chungbaek, Youngyun; Eubank, Stephen; Evrenosoğlu, Yaman; Lewis, Bryan; Lum, Kristian; Marathe, Achla; Marathe, Madhav; Mortveit, Henning; Parikh, Nidhi; Phadke, Arun; Reed, Jeffrey; Rivers, Caitlin; Saha, Sudip; Stretz, Paula; Swarup, Samarth; Thorp, James; Vullikanti, Anil; Xie, Dawen

2014-01-01

We present a synthetic information and modeling environment that can allow policy makers to study various counter-factual experiments in the event of a large human-initiated crisis. The specific scenario we consider is a ground detonation caused by an improvised nuclear device in a large urban region. In contrast to earlier work in this area that focuses largely on the prompt effects on human health and injury, we focus on co-evolution of individual and collective behavior and its interaction with the differentially damaged infrastructure. This allows us to study short term secondary and tertiary effects. The present environment is suitable for studying the dynamical outcomes over a two week period after the initial blast. A novel computing and data processing architecture is described; the architecture allows us to represent multiple co-evolving infrastructures and social networks at a highly resolved temporal, spatial, and individual scale. The representation allows us to study the emergent behavior of individuals as well as specific strategies to reduce casualties and injuries that exploit the spatial and temporal nature of the secondary and tertiary effects. A number of important conclusions are obtained using the modeling environment. For example, the studies decisively show that deploying ad hoc communication networks to reach individuals in the affected area is likely to have a significant impact on the overall casualties and injuries. PMID:25580055

PLANNING AND RESPONSE IN THE AFTERMATH OF A LARGE CRISIS: AN AGENT-BASED INFORMATICS FRAMEWORK*

PubMed

Barrett, Christopher; Bisset, Keith; Chandan, Shridhar; Chen, Jiangzhuo; Chungbaek, Youngyun; Eubank, Stephen; Evrenosoğlu, Yaman; Lewis, Bryan; Lum, Kristian; Marathe, Achla; Marathe, Madhav; Mortveit, Henning; Parikh, Nidhi; Phadke, Arun; Reed, Jeffrey; Rivers, Caitlin; Saha, Sudip; Stretz, Paula; Swarup, Samarth; Thorp, James; Vullikanti, Anil; Xie, Dawen

2013-01-01

We present a synthetic information and modeling environment that can allow policy makers to study various counter-factual experiments in the event of a large human-initiated crisis. The specific scenario we consider is a ground detonation caused by an improvised nuclear device in a large urban region. In contrast to earlier work in this area that focuses largely on the prompt effects on human health and injury, we focus on co-evolution of individual and collective behavior and its interaction with the differentially damaged infrastructure. This allows us to study short term secondary and tertiary effects. The present environment is suitable for studying the dynamical outcomes over a two week period after the initial blast. A novel computing and data processing architecture is described; the architecture allows us to represent multiple co-evolving infrastructures and social networks at a highly resolved temporal, spatial, and individual scale. The representation allows us to study the emergent behavior of individuals as well as specific strategies to reduce casualties and injuries that exploit the spatial and temporal nature of the secondary and tertiary effects. A number of important conclusions are obtained using the modeling environment. For example, the studies decisively show that deploying ad hoc communication networks to reach individuals in the affected area is likely to have a significant impact on the overall casualties and injuries.

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.

Randomizing bipartite networks: the case of the World Trade Web.

PubMed

Saracco, Fabio; Di Clemente, Riccardo; Gabrielli, Andrea; Squartini, Tiziano

2015-06-01

Within the last fifteen years, network theory has been successfully applied both to natural sciences and to socioeconomic disciplines. In particular, bipartite networks have been recognized to provide a particularly insightful representation of many systems, ranging from mutualistic networks in ecology to trade networks in economy, whence the need of a pattern detection-oriented analysis in order to identify statistically-significant structural properties. Such an analysis rests upon the definition of suitable null models, i.e. upon the choice of the portion of network structure to be preserved while randomizing everything else. However, quite surprisingly, little work has been done so far to define null models for real bipartite networks. The aim of the present work is to fill this gap, extending a recently-proposed method to randomize monopartite networks to bipartite networks. While the proposed formalism is perfectly general, we apply our method to the binary, undirected, bipartite representation of the World Trade Web, comparing the observed values of a number of structural quantities of interest with the expected ones, calculated via our randomization procedure. Interestingly, the behavior of the World Trade Web in this new representation is strongly different from the monopartite analogue, showing highly non-trivial patterns of self-organization.

Epidemic transmission on random mobile network with diverse infection periods

NASA Astrophysics Data System (ADS)

Li, Kezan; Yu, Hong; Zeng, Zhaorong; Ding, Yong; Ma, Zhongjun

2015-05-01

The heterogeneity of individual susceptibility and infectivity and time-varying topological structure are two realistic factors when we study epidemics on complex networks. Current research results have shown that the heterogeneity of individual susceptibility and infectivity can increase the epidemic threshold in a random mobile dynamical network with the same infection period. In this paper, we will focus on random mobile dynamical networks with diverse infection periods due to people's different constitutions and external circumstances. Theoretical results indicate that the epidemic threshold of the random mobile network with diverse infection periods is larger than the counterpart with the same infection period. Moreover, the heterogeneity of individual susceptibility and infectivity can play a significant impact on disease transmission. In particular, the homogeneity of individuals will avail to the spreading of epidemics. Numerical examples verify further our theoretical results very well.

Analytical connection between thresholds and immunization strategies of SIS model in random networks

NASA Astrophysics Data System (ADS)

Zhou, Ming-Yang; Xiong, Wen-Man; Liao, Hao; Wang, Tong; Wei, Zong-Wen; Fu, Zhong-Qian

2018-05-01

Devising effective strategies for hindering the propagation of viruses and protecting the population against epidemics is critical for public security and health. Despite a number of studies based on the susceptible-infected-susceptible (SIS) model devoted to this topic, we still lack a general framework to compare different immunization strategies in completely random networks. Here, we address this problem by suggesting a novel method based on heterogeneous mean-field theory for the SIS model. Our method builds the relationship between the thresholds and different immunization strategies in completely random networks. Besides, we provide an analytical argument that the targeted large-degree strategy achieves the best performance in random networks with arbitrary degree distribution. Moreover, the experimental results demonstrate the effectiveness of the proposed method in both artificial and real-world networks.

Patent citation network in nanotechnology (1976-2004)

NASA Astrophysics Data System (ADS)

Li, Xin; Chen, Hsinchun; Huang, Zan; Roco, Mihail C.

2007-06-01

The patent citation networks are described using critical node, core network, and network topological analysis. The main objective is understanding of the knowledge transfer processes between technical fields, institutions and countries. This includes identifying key influential players and subfields, the knowledge transfer patterns among them, and the overall knowledge transfer efficiency. The proposed framework is applied to the field of nanoscale science and engineering (NSE), including the citation networks of patent documents, submitting institutions, technology fields, and countries. The NSE patents were identified by keywords "full-text" searching of patents at the United States Patent and Trademark Office (USPTO). The analysis shows that the United States is the most important citation center in NSE research. The institution citation network illustrates a more efficient knowledge transfer between institutions than a random network. The country citation network displays a knowledge transfer capability as efficient as a random network. The technology field citation network and the patent document citation network exhibit a␣less efficient knowledge diffusion capability than a random network. All four citation networks show a tendency to form local citation clusters.

Application of stochastic processes in random growth and evolutionary dynamics

NASA Astrophysics Data System (ADS)

Oikonomou, Panagiotis

We study the effect of power-law distributed randomness on the dynamical behavior of processes such as stochastic growth patterns and evolution. First, we examine the geometrical properties of random shapes produced by a generalized stochastic Loewner Evolution driven by a superposition of a Brownian motion and a stable Levy process. The situation is defined by the usual stochastic Loewner Evolution parameter, kappa, as well as alpha which defines the power-law tail of the stable Levy distribution. We show that the properties of these patterns change qualitatively and singularly at critical values of kappa and alpha. It is reasonable to call such changes "phase transitions". These transitions occur as kappa passes through four and as alpha passes through one. Numerical simulations are used to explore the global scaling behavior of these patterns in each "phase". We show both analytically and numerically that the growth continues indefinitely in the vertical direction for alpha greater than 1, goes as logarithmically with time for alpha equals to 1, and saturates for alpha smaller than 1. The probability density has two different scales corresponding to directions along and perpendicular to the boundary. Scaling functions for the probability density are given for various limiting cases. Second, we study the effect of the architecture of biological networks on their evolutionary dynamics. In recent years, studies of the architecture of large networks have unveiled a common topology, called scale-free, in which a majority of the elements are poorly connected except for a small fraction of highly connected components. We ask how networks with distinct topologies can evolve towards a pre-established target phenotype through a process of random mutations and selection. We use networks of Boolean components as a framework to model a large class of phenotypes. Within this approach, we find that homogeneous random networks and scale-free networks exhibit drastically different evolutionary paths. While homogeneous random networks accumulate neutral mutations and evolve by sparse punctuated steps, scale-free networks evolve rapidly and continuously towards the target phenotype. Moreover, we show that scale-free networks always evolve faster than homogeneous random networks; remarkably, this property does not depend on the precise value of the topological parameter. By contrast, homogeneous random networks require a specific tuning of their topological parameter in order to optimize their fitness. This model suggests that the evolutionary paths of biological networks, punctuated or continuous, may solely be determined by the network topology.

Vulnerability of complex networks

NASA Astrophysics Data System (ADS)

Mishkovski, Igor; Biey, Mario; Kocarev, Ljupco

2011-01-01

We consider normalized average edge betweenness of a network as a metric of network vulnerability. We suggest that normalized average edge betweenness together with is relative difference when certain number of nodes and/or edges are removed from the network is a measure of network vulnerability, called vulnerability index. Vulnerability index is calculated for four synthetic networks: Erdős-Rényi (ER) random networks, Barabási-Albert (BA) model of scale-free networks, Watts-Strogatz (WS) model of small-world networks, and geometric random networks. Real-world networks for which vulnerability index is calculated include: two human brain networks, three urban networks, one collaboration network, and two power grid networks. We find that WS model of small-world networks and biological networks (human brain networks) are the most robust networks among all networks studied in the paper.

Random walks with long-range steps generated by functions of Laplacian matrices

NASA Astrophysics Data System (ADS)

Riascos, A. P.; Michelitsch, T. M.; Collet, B. A.; Nowakowski, A. F.; Nicolleau, F. C. G. A.

2018-04-01

In this paper, we explore different Markovian random walk strategies on networks with transition probabilities between nodes defined in terms of functions of the Laplacian matrix. We generalize random walk strategies with local information in the Laplacian matrix, that describes the connections of a network, to a dynamic determined by functions of this matrix. The resulting processes are non-local allowing transitions of the random walker from one node to nodes beyond its nearest neighbors. We find that only two types of Laplacian functions are admissible with distinct behaviors for long-range steps in the infinite network limit: type (i) functions generate Brownian motions, type (ii) functions Lévy flights. For this asymptotic long-range step behavior only the lowest non-vanishing order of the Laplacian function is relevant, namely first order for type (i), and fractional order for type (ii) functions. In the first part, we discuss spectral properties of the Laplacian matrix and a series of relations that are maintained by a particular type of functions that allow to define random walks on any type of undirected connected networks. Once described general properties, we explore characteristics of random walk strategies that emerge from particular cases with functions defined in terms of exponentials, logarithms and powers of the Laplacian as well as relations of these dynamics with non-local strategies like Lévy flights and fractional transport. Finally, we analyze the global capacity of these random walk strategies to explore networks like lattices and trees and different types of random and complex networks.

Distribution of shortest cycle lengths in random networks

NASA Astrophysics Data System (ADS)

Bonneau, Haggai; Hassid, Aviv; Biham, Ofer; Kühn, Reimer; Katzav, Eytan

2017-12-01

We present analytical results for the distribution of shortest cycle lengths (DSCL) in random networks. The approach is based on the relation between the DSCL and the distribution of shortest path lengths (DSPL). We apply this approach to configuration model networks, for which analytical results for the DSPL were obtained before. We first calculate the fraction of nodes in the network which reside on at least one cycle. Conditioning on being on a cycle, we provide the DSCL over ensembles of configuration model networks with degree distributions which follow a Poisson distribution (Erdős-Rényi network), degenerate distribution (random regular graph), and a power-law distribution (scale-free network). The mean and variance of the DSCL are calculated. The analytical results are found to be in very good agreement with the results of computer simulations.

Listening to the noise: random fluctuations reveal gene network parameters

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

Munsky, Brian; Khammash, Mustafa

2009-01-01

The cellular environment is abuzz with noise. The origin of this noise is attributed to the inherent random motion of reacting molecules that take part in gene expression and post expression interactions. In this noisy environment, clonal populations of cells exhibit cell-to-cell variability that frequently manifests as significant phenotypic differences within the cellular population. The stochastic fluctuations in cellular constituents induced by noise can be measured and their statistics quantified. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich sourcemore » of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. This establishes a potentially powerful approach for the identification of gene networks and offers a new window into the workings of these networks.« less

To cut or not to cut? Assessing the modular structure of brain networks.

PubMed

Chang, Yu-Teng; Pantazis, Dimitrios; Leahy, Richard M

2014-05-01

A wealth of methods has been developed to identify natural divisions of brain networks into groups or modules, with one of the most prominent being modularity. Compared with the popularity of methods to detect community structure, only a few methods exist to statistically control for spurious modules, relying almost exclusively on resampling techniques. It is well known that even random networks can exhibit high modularity because of incidental concentration of edges, even though they have no underlying organizational structure. Consequently, interpretation of community structure is confounded by the lack of principled and computationally tractable approaches to statistically control for spurious modules. In this paper we show that the modularity of random networks follows a transformed version of the Tracy-Widom distribution, providing for the first time a link between module detection and random matrix theory. We compute parametric formulas for the distribution of modularity for random networks as a function of network size and edge variance, and show that we can efficiently control for false positives in brain and other real-world networks. Copyright © 2014 Elsevier Inc. All rights reserved.

Tactical Network Load Balancing in Multi-Gateway Wireless Sensor Networks

DTIC Science & Technology

2013-12-01

writeup scrsz = get( 0 ,’ScreenSize’); %Creation of the random Sensor Network fig = figure(1); set(fig, ’Position’,[1 scrsz( 4 )*.25 scrsz(3)*.7...thesis writeup scrsz = get( 0 ,’ScreenSize’); %Creation of the random Sensor Network fig = figure(1); set(fig, ’Position’,[1 scrsz( 4 )*.25 scrsz(3)*.7...TYPE AND DATES COVERED Master’s Thesis 4 . TITLE AND SUBTITLE TACTICAL NETWORK LOAD BALANCING IN MULTI-GATEWAY WIRELESS SENSOR NETWORKS 5

A New Random Walk for Replica Detection in WSNs.

PubMed

Aalsalem, Mohammed Y; Khan, Wazir Zada; Saad, N M; Hossain, Md Shohrab; Atiquzzaman, Mohammed; Khan, Muhammad Khurram

2016-01-01

Wireless Sensor Networks (WSNs) are vulnerable to Node Replication attacks or Clone attacks. Among all the existing clone detection protocols in WSNs, RAWL shows the most promising results by employing Simple Random Walk (SRW). More recently, RAND outperforms RAWL by incorporating Network Division with SRW. Both RAND and RAWL have used SRW for random selection of witness nodes which is problematic because of frequently revisiting the previously passed nodes that leads to longer delays, high expenditures of energy with lower probability that witness nodes intersect. To circumvent this problem, we propose to employ a new kind of constrained random walk, namely Single Stage Memory Random Walk and present a distributed technique called SSRWND (Single Stage Memory Random Walk with Network Division). In SSRWND, single stage memory random walk is combined with network division aiming to decrease the communication and memory costs while keeping the detection probability higher. Through intensive simulations it is verified that SSRWND guarantees higher witness node security with moderate communication and memory overheads. SSRWND is expedient for security oriented application fields of WSNs like military and medical.

A New Random Walk for Replica Detection in WSNs

PubMed Central

Aalsalem, Mohammed Y.; Saad, N. M.; Hossain, Md. Shohrab; Atiquzzaman, Mohammed; Khan, Muhammad Khurram

2016-01-01

Wireless Sensor Networks (WSNs) are vulnerable to Node Replication attacks or Clone attacks. Among all the existing clone detection protocols in WSNs, RAWL shows the most promising results by employing Simple Random Walk (SRW). More recently, RAND outperforms RAWL by incorporating Network Division with SRW. Both RAND and RAWL have used SRW for random selection of witness nodes which is problematic because of frequently revisiting the previously passed nodes that leads to longer delays, high expenditures of energy with lower probability that witness nodes intersect. To circumvent this problem, we propose to employ a new kind of constrained random walk, namely Single Stage Memory Random Walk and present a distributed technique called SSRWND (Single Stage Memory Random Walk with Network Division). In SSRWND, single stage memory random walk is combined with network division aiming to decrease the communication and memory costs while keeping the detection probability higher. Through intensive simulations it is verified that SSRWND guarantees higher witness node security with moderate communication and memory overheads. SSRWND is expedient for security oriented application fields of WSNs like military and medical. PMID:27409082

Developing improved durum wheat germplasm by altering the cytoplasmic genome

USDA-ARS?s Scientific Manuscript database

In eukaryotic organisms, nuclear and cytoplasmic genomes interact to drive cellular functions. These genomes have co-evolved to form specific nuclear-cytoplasmic interactions that are essential to the origin, success, and evolution of diploid and polyploid species. Hundreds of genetic diseases in h...

Ant Species Differences Determined by Epistasis between Brood and Worker Genomes

PubMed Central

Linksvayer, Timothy A.

2007-01-01

Epistasis arising from physiological interactions between gene products often contributes to species differences, particularly those involved in reproductive isolation. In social organisms, phenotypes are influenced by the genotypes of multiple interacting individuals. In theory, social interactions can give rise to an additional type of epistasis between the genomes of social partners that can contribute to species differences. Using a full-factorial cross-fostering design with three species of closely related Temnothorax ants, I found that adult worker size was determined by an interaction between the genotypes of developing brood and care-giving workers, i.e. intergenomic epistasis. Such intergenomic social epistasis provides a strong signature of coevolution between social partners. These results demonstrate that just as physiologically interacting genes coevolve, diverge, and contribute to species differences, so do socially interacting genes. Coevolution and conflict between social partners, especially relatives such as parents and offspring, has long been recognized as having widespread evolutionary effects. This coevolutionary process may often result in coevolved socially-interacting gene complexes that contribute to species differences. PMID:17912371

The coevolution of recognition and social behavior.

PubMed

Smead, Rory; Forber, Patrick

2016-05-26

Recognition of behavioral types can facilitate the evolution of cooperation by enabling altruistic behavior to be directed at other cooperators and withheld from defectors. While much is known about the tendency for recognition to promote cooperation, relatively little is known about whether such a capacity can coevolve with the social behavior it supports. Here we use evolutionary game theory and multi-population dynamics to model the coevolution of social behavior and recognition. We show that conditional harming behavior enables the evolution and stability of social recognition, whereas conditional helping leads to a deterioration of recognition ability. Expanding the model to include a complex game where both helping and harming interactions are possible, we find that conditional harming behavior can stabilize recognition, and thereby lead to the evolution of conditional helping. Our model identifies a novel hypothesis for the evolution of cooperation: conditional harm may have coevolved with recognition first, thereby helping to establish the mechanisms necessary for the evolution of cooperation.

The coevolution of recognition and social behavior

PubMed Central

Smead, Rory; Forber, Patrick

2016-01-01

Recognition of behavioral types can facilitate the evolution of cooperation by enabling altruistic behavior to be directed at other cooperators and withheld from defectors. While much is known about the tendency for recognition to promote cooperation, relatively little is known about whether such a capacity can coevolve with the social behavior it supports. Here we use evolutionary game theory and multi-population dynamics to model the coevolution of social behavior and recognition. We show that conditional harming behavior enables the evolution and stability of social recognition, whereas conditional helping leads to a deterioration of recognition ability. Expanding the model to include a complex game where both helping and harming interactions are possible, we find that conditional harming behavior can stabilize recognition, and thereby lead to the evolution of conditional helping. Our model identifies a novel hypothesis for the evolution of cooperation: conditional harm may have coevolved with recognition first, thereby helping to establish the mechanisms necessary for the evolution of cooperation. PMID:27225673

Optimization of the p-xylene oxidation process by a multi-objective differential evolution algorithm with adaptive parameters co-derived with the population-based incremental learning algorithm

NASA Astrophysics Data System (ADS)

Guo, Zhan; Yan, Xuefeng

2018-04-01

Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective differential evolution (MODE) algorithm co-evolved with the population-based incremental learning (PBIL) algorithm, called PBMODE, is proposed. The PBMODE algorithm was designed as a co-evolutionary system. Each individual has its own parameter individual, which is co-evolved by PBIL. PBIL uses statistical analysis to build a model based on the corresponding symbiotic individuals of the superior original individuals during the main evolutionary process. The results of simulations and statistical analysis indicate that the overall performance of the PBMODE algorithm is better than that of the compared algorithms and it can be used to optimize the operating conditions of the p-xylene oxidation process effectively and efficiently.

An analysis of parameter sensitivities of preference-inspired co-evolutionary algorithms

NASA Astrophysics Data System (ADS)

Wang, Rui; Mansor, Maszatul M.; Purshouse, Robin C.; Fleming, Peter J.

2015-10-01

Many-objective optimisation problems remain challenging for many state-of-the-art multi-objective evolutionary algorithms. Preference-inspired co-evolutionary algorithms (PICEAs) which co-evolve the usual population of candidate solutions with a family of decision-maker preferences during the search have been demonstrated to be effective on such problems. However, it is unknown whether PICEAs are robust with respect to the parameter settings. This study aims to address this question. First, a global sensitivity analysis method - the Sobol' variance decomposition method - is employed to determine the relative importance of the parameters controlling the performance of PICEAs. Experimental results show that the performance of PICEAs is controlled for the most part by the number of function evaluations. Next, we investigate the effect of key parameters identified from the Sobol' test and the genetic operators employed in PICEAs. Experimental results show improved performance of the PICEAs as more preferences are co-evolved. Additionally, some suggestions for genetic operator settings are provided for non-expert users.

Network problem threshold

NASA Technical Reports Server (NTRS)

Gejji, Raghvendra, R.

1992-01-01

Network transmission errors such as collisions, CRC errors, misalignment, etc. are statistical in nature. Although errors can vary randomly, a high level of errors does indicate specific network problems, e.g. equipment failure. In this project, we have studied the random nature of collisions theoretically as well as by gathering statistics, and established a numerical threshold above which a network problem is indicated with high probability.

A Simulation Study Comparing Epidemic Dynamics on Exponential Random Graph and Edge-Triangle Configuration Type Contact Network Models

PubMed Central

Rolls, David A.; Wang, Peng; McBryde, Emma; Pattison, Philippa; Robins, Garry

2015-01-01

We compare two broad types of empirically grounded random network models in terms of their abilities to capture both network features and simulated Susceptible-Infected-Recovered (SIR) epidemic dynamics. The types of network models are exponential random graph models (ERGMs) and extensions of the configuration model. We use three kinds of empirical contact networks, chosen to provide both variety and realistic patterns of human contact: a highly clustered network, a bipartite network and a snowball sampled network of a “hidden population”. In the case of the snowball sampled network we present a novel method for fitting an edge-triangle model. In our results, ERGMs consistently capture clustering as well or better than configuration-type models, but the latter models better capture the node degree distribution. Despite the additional computational requirements to fit ERGMs to empirical networks, the use of ERGMs provides only a slight improvement in the ability of the models to recreate epidemic features of the empirical network in simulated SIR epidemics. Generally, SIR epidemic results from using configuration-type models fall between those from a random network model (i.e., an Erdős-Rényi model) and an ERGM. The addition of subgraphs of size four to edge-triangle type models does improve agreement with the empirical network for smaller densities in clustered networks. Additional subgraphs do not make a noticeable difference in our example, although we would expect the ability to model cliques to be helpful for contact networks exhibiting household structure. PMID:26555701

Self-avoiding walks on scale-free networks

NASA Astrophysics Data System (ADS)

Herrero, Carlos P.

2005-01-01

Several kinds of walks on complex networks are currently used to analyze search and navigation in different systems. Many analytical and computational results are known for random walks on such networks. Self-avoiding walks (SAW’s) are expected to be more suitable than unrestricted random walks to explore various kinds of real-life networks. Here we study long-range properties of random SAW’s on scale-free networks, characterized by a degree distribution P (k) ˜ k-γ . In the limit of large networks (system size N→∞ ), the average number sn of SAW’s starting from a generic site increases as μn , with μ= < k2 > / -1 . For finite N , sn is reduced due to the presence of loops in the network, which causes the emergence of attrition of the paths. For kinetic growth walks, the average maximum length increases as a power of the system size: ˜ Nα , with an exponent α increasing as the parameter γ is raised. We discuss the dependence of α on the minimum allowed degree in the network. A similar power-law dependence is found for the mean self-intersection length of nonreversal random walks. Simulation results support our approximate analytical calculations.

Impact of degree heterogeneity on the behavior of trapping in Koch networks

NASA Astrophysics Data System (ADS)

Zhang, Zhongzhi; Gao, Shuyang; Xie, Wenlei

2010-12-01

Previous work shows that the mean first-passage time (MFPT) for random walks to a given hub node (node with maximum degree) in uncorrelated random scale-free networks is closely related to the exponent γ of power-law degree distribution P(k )˜k-γ, which describes the extent of heterogeneity of scale-free network structure. However, extensive empirical research indicates that real networked systems also display ubiquitous degree correlations. In this paper, we address the trapping issue on the Koch networks, which is a special random walk with one trap fixed at a hub node. The Koch networks are power-law with the characteristic exponent γ in the range between 2 and 3, they are either assortative or disassortative. We calculate exactly the MFPT that is the average of first-passage time from all other nodes to the trap. The obtained explicit solution shows that in large networks the MFPT varies lineally with node number N, which is obviously independent of γ and is sharp contrast to the scaling behavior of MFPT observed for uncorrelated random scale-free networks, where γ influences qualitatively the MFPT of trapping problem.

Selective randomized load balancing and mesh networks with changing demands

NASA Astrophysics Data System (ADS)

Shepherd, F. B.; Winzer, P. J.

2006-05-01

We consider the problem of building cost-effective networks that are robust to dynamic changes in demand patterns. We compare several architectures using demand-oblivious routing strategies. Traditional approaches include single-hop architectures based on a (static or dynamic) circuit-switched core infrastructure and multihop (packet-switched) architectures based on point-to-point circuits in the core. To address demand uncertainty, we seek minimum cost networks that can carry the class of hose demand matrices. Apart from shortest-path routing, Valiant's randomized load balancing (RLB), and virtual private network (VPN) tree routing, we propose a third, highly attractive approach: selective randomized load balancing (SRLB). This is a blend of dual-hop hub routing and randomized load balancing that combines the advantages of both architectures in terms of network cost, delay, and delay jitter. In particular, we give empirical analyses for the cost (in terms of transport and switching equipment) for the discussed architectures, based on three representative carrier networks. Of these three networks, SRLB maintains the resilience properties of RLB while achieving significant cost reduction over all other architectures, including RLB and multihop Internet protocol/multiprotocol label switching (IP/MPLS) networks using VPN-tree routing.

Global mean first-passage times of random walks on complex networks.

PubMed

Tejedor, V; Bénichou, O; Voituriez, R

2009-12-01

We present a general framework, applicable to a broad class of random walks on complex networks, which provides a rigorous lower bound for the mean first-passage time of a random walker to a target site averaged over its starting position, the so-called global mean first-passage time (GMFPT). This bound is simply expressed in terms of the equilibrium distribution at the target and implies a minimal scaling of the GMFPT with the network size. We show that this minimal scaling, which can be arbitrarily slow, is realized under the simple condition that the random walk is transient at the target site and independently of the small-world, scale-free, or fractal properties of the network. Last, we put forward that the GMFPT to a specific target is not a representative property of the network since the target averaged GMFPT satisfies much more restrictive bounds.

A scaling law for random walks on networks

PubMed Central

Perkins, Theodore J.; Foxall, Eric; Glass, Leon; Edwards, Roderick

2014-01-01

The dynamics of many natural and artificial systems are well described as random walks on a network: the stochastic behaviour of molecules, traffic patterns on the internet, fluctuations in stock prices and so on. The vast literature on random walks provides many tools for computing properties such as steady-state probabilities or expected hitting times. Previously, however, there has been no general theory describing the distribution of possible paths followed by a random walk. Here, we show that for any random walk on a finite network, there are precisely three mutually exclusive possibilities for the form of the path distribution: finite, stretched exponential and power law. The form of the distribution depends only on the structure of the network, while the stepping probabilities control the parameters of the distribution. We use our theory to explain path distributions in domains such as sports, music, nonlinear dynamics and stochastic chemical kinetics. PMID:25311870

A scaling law for random walks on networks

NASA Astrophysics Data System (ADS)

Perkins, Theodore J.; Foxall, Eric; Glass, Leon; Edwards, Roderick

2014-10-01

The dynamics of many natural and artificial systems are well described as random walks on a network: the stochastic behaviour of molecules, traffic patterns on the internet, fluctuations in stock prices and so on. The vast literature on random walks provides many tools for computing properties such as steady-state probabilities or expected hitting times. Previously, however, there has been no general theory describing the distribution of possible paths followed by a random walk. Here, we show that for any random walk on a finite network, there are precisely three mutually exclusive possibilities for the form of the path distribution: finite, stretched exponential and power law. The form of the distribution depends only on the structure of the network, while the stepping probabilities control the parameters of the distribution. We use our theory to explain path distributions in domains such as sports, music, nonlinear dynamics and stochastic chemical kinetics.

A scaling law for random walks on networks.

PubMed

Perkins, Theodore J; Foxall, Eric; Glass, Leon; Edwards, Roderick

2014-10-14

The dynamics of many natural and artificial systems are well described as random walks on a network: the stochastic behaviour of molecules, traffic patterns on the internet, fluctuations in stock prices and so on. The vast literature on random walks provides many tools for computing properties such as steady-state probabilities or expected hitting times. Previously, however, there has been no general theory describing the distribution of possible paths followed by a random walk. Here, we show that for any random walk on a finite network, there are precisely three mutually exclusive possibilities for the form of the path distribution: finite, stretched exponential and power law. The form of the distribution depends only on the structure of the network, while the stepping probabilities control the parameters of the distribution. We use our theory to explain path distributions in domains such as sports, music, nonlinear dynamics and stochastic chemical kinetics.

Complex systems: physics beyond physics

NASA Astrophysics Data System (ADS)

Holovatch, Yurij; Kenna, Ralph; Thurner, Stefan

2017-03-01

Complex systems are characterised by specific time-dependent interactions among their many constituents. As a consequence they often manifest rich, non-trivial and unexpected behaviour. Examples arise both in the physical and non-physical worlds. The study of complex systems forms a new interdisciplinary research area that cuts across physics, biology, ecology, economics, sociology, and the humanities. In this paper we review the essence of complex systems from a physicists' point of view, and try to clarify what makes them conceptually different from systems that are traditionally studied in physics. Our goal is to demonstrate how the dynamics of such systems may be conceptualised in quantitative and predictive terms by extending notions from statistical physics and how they can often be captured in a framework of co-evolving multiplex network structures. We mention three areas of complex-systems science that are currently studied extensively, the science of cities, dynamics of societies, and the representation of texts as evolutionary objects. We discuss why these areas form complex systems in the above sense. We argue that there exists plenty of new ground for physicists to explore and that methodical and conceptual progress is needed most.

Small-World Network Spectra in Mean-Field Theory

NASA Astrophysics Data System (ADS)

Grabow, Carsten; Grosskinsky, Stefan; Timme, Marc

2012-05-01

Collective dynamics on small-world networks emerge in a broad range of systems with their spectra characterizing fundamental asymptotic features. Here we derive analytic mean-field predictions for the spectra of small-world models that systematically interpolate between regular and random topologies by varying their randomness. These theoretical predictions agree well with the actual spectra (obtained by numerical diagonalization) for undirected and directed networks and from fully regular to strongly random topologies. These results may provide analytical insights to empirically found features of dynamics on small-world networks from various research fields, including biology, physics, engineering, and social science.

How Fast Can Networks Synchronize? A Random Matrix Theory Approach

NASA Astrophysics Data System (ADS)

Timme, Marc; Wolf, Fred; Geisel, Theo

2004-03-01

Pulse-coupled oscillators constitute a paradigmatic class of dynamical systems interacting on networks because they model a variety of biological systems including flashing fireflies and chirping crickets as well as pacemaker cells of the heart and neural networks. Synchronization is one of the most simple and most prevailing kinds of collective dynamics on such networks. Here we study collective synchronization [1] of pulse-coupled oscillators interacting on asymmetric random networks. Using random matrix theory we analytically determine the speed of synchronization in such networks in dependence on the dynamical and network parameters [2]. The speed of synchronization increases with increasing coupling strengths. Surprisingly, however, it stays finite even for infinitely strong interactions. The results indicate that the speed of synchronization is limited by the connectivity of the network. We discuss the relevance of our findings to general equilibration processes on complex networks. [5mm] [1] M. Timme, F. Wolf, T. Geisel, Phys. Rev. Lett. 89:258701 (2002). [2] M. Timme, F. Wolf, T. Geisel, cond-mat/0306512 (2003).

Progress in Open-World, Integrative, Collaborative Science Data Platforms (Invited)

NASA Astrophysics Data System (ADS)

Fox, P. A.

2013-12-01

As collaborative, or network science spreads into more Earth and space science fields, both the participants and their funders have expressed a very strong desire for highly functional data and information capabilities that are a) easy to use, b) integrated in a variety of ways, c) leverage prior investments and keep pace with rapid technical change, and d) are not expensive or time-consuming to build or maintain. In response, and based on our accumulated experience over the last decade and a maturing of several key technical approaches, we have adapted, extended, and integrated several open source applications and frameworks that handle major portions of functionality for these platforms. At minimum, these functions include: an object-type repository, collaboration tools, an ability to identify and manage all key entities in the platform, and an integrated portal to manage diverse content and applications, with varied access levels and privacy options. At a conceptual level, science networks (even small ones) deal with people, and many intellectual artifacts produced or consumed in research, organizational and/our outreach activities, as well as the relations among them. Increasingly these networks are modeled as knowledge networks, i.e. graphs with named and typed relations among the 'nodes'. Nodes can be people, organizations, datasets, events, presentations, publications, videos, meetings, reports, groups, and more. In this heterogeneous ecosystem, it is also important to use a set of common informatics approaches to co-design and co-evolve the needed science data platforms based on what real people want to use them for. In this contribution, we present our methods and results for information modeling, adapting, integrating and evolving a networked data science and information architecture based on several open source technologies (Drupal, VIVO, the Comprehensive Knowledge Archive Network; CKAN, and the Global Handle System; GHS). In particular we present both the instantiation of this data platform for the Deep Carbon Observatory, including key functional and non-functional attributes, how the smart mediation among the components is modeled and managed, and discuss its general applicability.

Perturbation propagation in random and evolved Boolean networks

NASA Astrophysics Data System (ADS)

Fretter, Christoph; Szejka, Agnes; Drossel, Barbara

2009-03-01

In this paper, we investigate the propagation of perturbations in Boolean networks by evaluating the Derrida plot and its modifications. We show that even small random Boolean networks agree well with the predictions of the annealed approximation, but nonrandom networks show a very different behaviour. We focus on networks that were evolved for high dynamical robustness. The most important conclusion is that the simple distinction between frozen, critical and chaotic networks is no longer useful, since such evolved networks can display the properties of all three types of networks. Furthermore, we evaluate a simplified empirical network and show how its specific state space properties are reflected in the modified Derrida plots.

Spread of information and infection on finite random networks

NASA Astrophysics Data System (ADS)

Isham, Valerie; Kaczmarska, Joanna; Nekovee, Maziar

2011-04-01

The modeling of epidemic-like processes on random networks has received considerable attention in recent years. While these processes are inherently stochastic, most previous work has been focused on deterministic models that ignore important fluctuations that may persist even in the infinite network size limit. In a previous paper, for a class of epidemic and rumor processes, we derived approximate models for the full probability distribution of the final size of the epidemic, as opposed to only mean values. In this paper we examine via direct simulations the adequacy of the approximate model to describe stochastic epidemics and rumors on several random network topologies: homogeneous networks, Erdös-Rényi (ER) random graphs, Barabasi-Albert scale-free networks, and random geometric graphs. We find that the approximate model is reasonably accurate in predicting the probability of spread. However, the position of the threshold and the conditional mean of the final size for processes near the threshold are not well described by the approximate model even in the case of homogeneous networks. We attribute this failure to the presence of other structural properties beyond degree-degree correlations, and in particular clustering, which are present in any finite network but are not incorporated in the approximate model. In order to test this “hypothesis” we perform additional simulations on a set of ER random graphs where degree-degree correlations and clustering are separately and independently introduced using recently proposed algorithms from the literature. Our results show that even strong degree-degree correlations have only weak effects on the position of the threshold and the conditional mean of the final size. On the other hand, the introduction of clustering greatly affects both the position of the threshold and the conditional mean. Similar analysis for the Barabasi-Albert scale-free network confirms the significance of clustering on the dynamics of rumor spread. For this network, though, with its highly skewed degree distribution, the addition of positive correlation had a much stronger effect on the final size distribution than was found for the simple random graph.

Parameters affecting the resilience of scale-free networks to random failures.

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

Link, Hamilton E.; LaViolette, Randall A.; Lane, Terran

2005-09-01

It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. in (1) study scale-free networks including some which approximate the measured degree distribution of the Internet. Their results suggest that if each node in this network failed independently with probability 0.99, most of the remaining nodes would still be connected in a giant component. In this paper, we show that a large and important subclass of scale-free networks are not robust to massive numbers of random node deletions. In particular, we study scale-free networks which have minimum node degreemore » of 1 and a power-law degree distribution beginning with nodes of degree 1 (power-law networks). We show that, in a power-law network approximating the Internet's reported distribution, when the probability of deletion of each node is 0.5 only about 25% of the surviving nodes in the network remain connected in a giant component, and the giant component does not persist beyond a critical failure rate of 0.9. The new result is partially due to improved analytical accommodation of the large number of degree-0 nodes that result after node deletions. Our results apply to power-law networks with a wide range of power-law exponents, including Internet-like networks. We give both analytical and empirical evidence that such networks are not generally robust to massive random node deletions.« less

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

PubMed

Zheng, Muhua; Lü, Linyuan; Zhao, Ming

2013-07-01

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

Coevolutionary Free Lunches

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Macready, William G.

2005-01-01

Recent work on the foundations of optimization has begun to uncover its underlying rich structure. In particular, the "No Free Lunch" (NFL) theorems [WM97] state that any two algorithms are equivalent when their performance is averaged across all possible problems. This highlights the need for exploiting problem-specific knowledge to achieve better than random performance. In this paper we present a general framework covering most search scenarios. In addition to the optimization scenarios addressed in the NFL results, this framework covers multi-armed bandit problems and evolution of multiple co-evolving agents. As a particular instance of the latter, it covers "self-play" problems. In these problems the agents work together to produce a champion, who then engages one or more antagonists in a subsequent multi-player game In contrast to the traditional optimization case where the NFL results hold, we show that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems. However in the typical coevolutionary scenarios encountered in biology, where there is no champion, NFL still holds.

Routing in Networks with Random Topologies

NASA Technical Reports Server (NTRS)

Bambos, Nicholas

1997-01-01

We examine the problems of routing and server assignment in networks with random connectivities. In such a network the basic topology is fixed, but during each time slot and for each of tis input queues, each server (node) is either connected to or disconnected from each of its queues with some probability.

Gossip and Distributed Kalman Filtering: Weak Consensus Under Weak Detectability

NASA Astrophysics Data System (ADS)

Kar, Soummya; Moura, José M. F.

2011-04-01

The paper presents the gossip interactive Kalman filter (GIKF) for distributed Kalman filtering for networked systems and sensor networks, where inter-sensor communication and observations occur at the same time-scale. The communication among sensors is random; each sensor occasionally exchanges its filtering state information with a neighbor depending on the availability of the appropriate network link. We show that under a weak distributed detectability condition: 1. the GIKF error process remains stochastically bounded, irrespective of the instability properties of the random process dynamics; and 2. the network achieves \\emph{weak consensus}, i.e., the conditional estimation error covariance at a (uniformly) randomly selected sensor converges in distribution to a unique invariant measure on the space of positive semi-definite matrices (independent of the initial state.) To prove these results, we interpret the filtered states (estimates and error covariances) at each node in the GIKF as stochastic particles with local interactions. We analyze the asymptotic properties of the error process by studying as a random dynamical system the associated switched (random) Riccati equation, the switching being dictated by a non-stationary Markov chain on the network graph.

Discrete-time systems with random switches: From systems stability to networks synchronization.

PubMed

Guo, Yao; Lin, Wei; Ho, Daniel W C

2016-03-01

In this article, we develop some approaches, which enable us to more accurately and analytically identify the essential patterns that guarantee the almost sure stability of discrete-time systems with random switches. We allow for the case that the elements in the switching connection matrix even obey some unbounded and continuous-valued distributions. In addition to the almost sure stability, we further investigate the almost sure synchronization in complex dynamical networks consisting of randomly connected nodes. Numerical examples illustrate that a chaotic dynamics in the synchronization manifold is preserved when statistical parameters enter some almost sure synchronization region established by the developed approach. Moreover, some delicate configurations are considered on probability space for ensuring synchronization in networks whose nodes are described by nonlinear maps. Both theoretical and numerical results on synchronization are presented by setting only a few random connections in each switch duration. More interestingly, we analytically find it possible to achieve almost sure synchronization in the randomly switching complex networks even with very large population sizes, which cannot be easily realized in non-switching but deterministically connected networks.

Discrete-time systems with random switches: From systems stability to networks synchronization

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

Guo, Yao; Lin, Wei, E-mail: wlin@fudan.edu.cn; Shanghai Key Laboratory of Contemporary Applied Mathematics, LMNS, and Shanghai Center for Mathematical Sciences, Shanghai 200433

2016-03-15

In this article, we develop some approaches, which enable us to more accurately and analytically identify the essential patterns that guarantee the almost sure stability of discrete-time systems with random switches. We allow for the case that the elements in the switching connection matrix even obey some unbounded and continuous-valued distributions. In addition to the almost sure stability, we further investigate the almost sure synchronization in complex dynamical networks consisting of randomly connected nodes. Numerical examples illustrate that a chaotic dynamics in the synchronization manifold is preserved when statistical parameters enter some almost sure synchronization region established by the developedmore » approach. Moreover, some delicate configurations are considered on probability space for ensuring synchronization in networks whose nodes are described by nonlinear maps. Both theoretical and numerical results on synchronization are presented by setting only a few random connections in each switch duration. More interestingly, we analytically find it possible to achieve almost sure synchronization in the randomly switching complex networks even with very large population sizes, which cannot be easily realized in non-switching but deterministically connected networks.« less

Weighted Scaling in Non-growth Random Networks

NASA Astrophysics Data System (ADS)

Chen, Guang; Yang, Xu-Hua; Xu, Xin-Li

2012-09-01

We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in non-growth random networks. In this model, we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it. The network evolves according to a vertex strength preferential selection mechanism. During the evolution process, the network always holds its total number of vertices and its total number of single-edges constantly. We show analytically and numerically that a network will form steady scale-free distributions with our model. The results show that a weighted non-growth random network can evolve into scale-free state. It is interesting that the network also obtains the character of an exponential edge weight distribution. Namely, coexistence of scale-free distribution and exponential distribution emerges.

Symmetries and synchronization in multilayer random networks

NASA Astrophysics Data System (ADS)

Saa, Alberto

2018-04-01

In the light of the recently proposed scenario of asymmetry-induced synchronization (AISync), in which dynamical uniformity and consensus in a distributed system would demand certain asymmetries in the underlying network, we investigate here the influence of some regularities in the interlayer connection patterns on the synchronization properties of multilayer random networks. More specifically, by considering a Stuart-Landau model of complex oscillators with random frequencies, we report for multilayer networks a dynamical behavior that could be also classified as a manifestation of AISync. We show, namely, that the presence of certain symmetries in the interlayer connection pattern tends to diminish the synchronization capability of the whole network or, in other words, asymmetries in the interlayer connections would enhance synchronization in such structured networks. Our results might help the understanding not only of the AISync mechanism itself but also its possible role in the determination of the interlayer connection pattern of multilayer and other structured networks with optimal synchronization properties.

Technology-Enabled Crime, Policing and Security

ERIC Educational Resources Information Center

McQuade, Sam

2006-01-01

Crime, policing and security are enabled by and co-evolve with technologies that make them possible. As criminals compete with security and policing officials for technological advantage perpetually complex crime, policing and security results in relatively confusing and therefore unmanageable threats to society. New, adaptive and ordinary crimes…

Biogeography of mutualistic fungi cultivated by leafcutter ants

USDA-ARS?s Scientific Manuscript database

Leafcutter ants propagate co-evolving fungi for food. The nearly 50 species of leafcutter ants (Atta, Acromyrmex) range from Argentina to the USA, with the greatest species diversity in southern South America. We elucidate the biogeography of fungi cultivated by leafcutter ants using DNA-sequence an...

Cache-site selection in Clark's Nutcracker (Nucifraga columbiana)

Treesearch

Teresa J. Lorenz; Kimberly A. Sullivan; Amanda V. Bakian; Carol A. Aubry

2011-01-01

Clark's Nutcracker (Nucifraga Columbiana) is one of the most specialized scatter-hoarding birds, considered a seed disperser for four species of pines (Pinus spp.), as well as an obligate coevolved mutualist of White bark Pine (P. albicaulis). Cache-site selection has not been formally studied in Clark...

Comparative genomics of a plant-parasitic nematode endosymbiont suggest a role in nutritional symbiosis

USDA-ARS?s Scientific Manuscript database

Bacterial mutualists can increase the biochemical capacity of animals. Highly co-evolved nutritional mutualists do this by synthesizing nutrients missing from the host's diet. Genomics tools have recently advanced the study of these partnerships. Here we examined the endosymbiont Xiphinematobacter (...

Sensitivity and network topology in chemical reaction systems

NASA Astrophysics Data System (ADS)

Okada, Takashi; Mochizuki, Atsushi

2017-08-01

In living cells, biochemical reactions are catalyzed by specific enzymes and connect to one another by sharing substrates and products, forming complex networks. In our previous studies, we established a framework determining the responses to enzyme perturbations only from network topology, and then proved a theorem, called the law of localization, explaining response patterns in terms of network topology. In this paper, we generalize these results to reaction networks with conserved concentrations, which allows us to study any reaction system. We also propose network characteristics quantifying robustness. We compare E. coli metabolic network with randomly rewired networks, and find that the robustness of the E. coli network is significantly higher than that of the random networks.

Conductivity of Nanowire Arrays under Random and Ordered Orientation Configurations

PubMed Central

Jagota, Milind; Tansu, Nelson

2015-01-01

A computational model was developed to analyze electrical conductivity of random metal nanowire networks. It was demonstrated for the first time through use of this model that a performance gain in random metal nanowire networks can be achieved by slightly restricting nanowire orientation. It was furthermore shown that heavily ordered configurations do not outperform configurations with some degree of randomness; randomness in the case of metal nanowire orientations acts to increase conductivity. PMID:25976936

Age differences in behavioral and neural correlates of proactive interference: Disentangling the role of overall working memory performance.

PubMed

Loosli, Sandra V; Rahm, Benjamin; Unterrainer, Josef M; Mader, Irina; Weiller, Cornelius; Kaller, Christoph P

2016-02-15

Reliable performance in working memory (WM) critically depends on the ability to resist proactive interference (PI) from previously relevant WM contents. Both WM performance and PI susceptibility are subject to cognitive decline at older adult age. However, the behavioral and neural processes underlying these co-evolving developmental changes and their potential interdependencies are not yet understood. Here, we investigated PI using a recent-probes WM paradigm and functional MRI in a cross-sectional sample of younger (n=18, 10 female, 23.4 ± 2.7 years) and older adults (n=18, 10 female, 70.2 ± 2.7 years). As expected, older adults showed lower WM performance and higher PI susceptibility than younger adults. Resolution of PI activated a mainly bilateral frontal network across all participants. Significant interactions with age indicated reduced neural activation in older adults for PI resolution. A second analysis in a selection of younger and older adults (n=12 each) with matched WM performance also revealed significant differences in PI between both age groups and - on a descriptive level - again a hypo-activation of the older adults' PI network. But the differential effect of age on the neural PI effects did not reach significance in this smaller sample most likely to the reduced statistical power. However, given the highly similar patterns in both the overall and the WM-matched samples, we propose that the hypo-activation of the PI network in the older adults may not be attributable to age-related differences in overall WM performance, hence suggesting that higher PI susceptibility in older adult age does not directly depend on their lower WM performance. Copyright © 2015 Elsevier Inc. All rights reserved.

Protein interaction networks at the host-microbe interface in Diaphorina citri, the insect vector of the citrus greening pathogen.

PubMed

Ramsey, J S; Chavez, J D; Johnson, R; Hosseinzadeh, S; Mahoney, J E; Mohr, J P; Robison, F; Zhong, X; Hall, D G; MacCoss, M; Bruce, J; Cilia, M

2017-02-01

The Asian citrus psyllid ( Diaphorina citri) is the insect vector responsible for the worldwide spread of ' Candidatus Liberibacter asiaticus' (CLas), the bacterial pathogen associated with citrus greening disease. Developmental changes in the insect vector impact pathogen transmission, such that D. citri transmission of CLas is more efficient when bacteria are acquired by nymphs when compared with adults. We hypothesize that expression changes in the D. citri immune system and commensal microbiota occur during development and regulate vector competency. In support of this hypothesis, more proteins, with greater fold changes, were differentially expressed in response to CLas in adults when compared with nymphs, including insect proteins involved in bacterial adhesion and immunity. Compared with nymphs, adult insects had a higher titre of CLas and the bacterial endosymbionts Wolbachia, Profftella and Carsonella. All Wolbachia and Profftella proteins differentially expressed between nymphs and adults are upregulated in adults, while most differentially expressed Carsonella proteins are upregulated in nymphs. Discovery of protein interaction networks has broad applicability to the study of host-microbe relationships. Using protein interaction reporter technology, a D. citri haemocyanin protein highly upregulated in response to CLas was found to physically interact with the CLas coenzyme A (CoA) biosynthesis enzyme phosphopantothenoylcysteine synthetase/decarboxylase. CLas pantothenate kinase, which catalyses the rate-limiting step of CoA biosynthesis, was found to interact with a D. citri myosin protein. Two Carsonella enzymes involved in histidine and tryptophan biosynthesis were found to physically interact with D. citri proteins. These co-evolved protein interaction networks at the host-microbe interface are highly specific targets for controlling the insect vector responsible for the spread of citrus greening.

The Drosophila transcriptional network is structured by microbiota.

PubMed

Dobson, Adam J; Chaston, John M; Douglas, Angela E

2016-11-25

Resident microorganisms (microbiota) have far-reaching effects on the biology of their animal hosts, with major consequences for the host's health and fitness. A full understanding of microbiota-dependent gene regulation requires analysis of the overall architecture of the host transcriptome, by identifying suites of genes that are expressed synchronously. In this study, we investigated the impact of the microbiota on gene coexpression in Drosophila. Our transcriptomic analysis, of 17 lines representative of the global genetic diversity of Drosophila, yielded a total of 11 transcriptional modules of co-expressed genes. For seven of these modules, the strength of the transcriptional network (defined as gene-gene coexpression) differed significantly between flies bearing a defined gut microbiota (gnotobiotic flies) and flies reared under microbiologically sterile conditions (axenic flies). Furthermore, gene coexpression was uniformly stronger in these microbiota-dependent modules than in both the microbiota-independent modules in gnotobiotic flies and all modules in axenic flies, indicating that the presence of the microbiota directs gene regulation in a subset of the transcriptome. The genes constituting the microbiota-dependent transcriptional modules include regulators of growth, metabolism and neurophysiology, previously implicated in mediating phenotypic effects of microbiota on Drosophila phenotype. Together these results provide the first evidence that the microbiota enhances the coexpression of specific and functionally-related genes relative to the animal's intrinsic baseline level of coexpression. Our system-wide analysis demonstrates that the presence of microbiota enhances gene coexpression, thereby structuring the transcriptional network in the animal host. This finding has potentially major implications for understanding of the mechanisms by which microbiota affect host health and fitness, and the ways in which hosts and their resident microbiota coevolve.

Protein interaction networks at the host–microbe interface in Diaphorina citri, the insect vector of the citrus greening pathogen

PubMed Central

Chavez, J. D.; Johnson, R.; Hosseinzadeh, S.; Mahoney, J. E.; Mohr, J. P.; Robison, F.; Zhong, X.; Hall, D. G.; MacCoss, M.; Bruce, J.; Cilia, M.

2017-01-01

The Asian citrus psyllid (Diaphorina citri) is the insect vector responsible for the worldwide spread of ‘Candidatus Liberibacter asiaticus’ (CLas), the bacterial pathogen associated with citrus greening disease. Developmental changes in the insect vector impact pathogen transmission, such that D. citri transmission of CLas is more efficient when bacteria are acquired by nymphs when compared with adults. We hypothesize that expression changes in the D. citri immune system and commensal microbiota occur during development and regulate vector competency. In support of this hypothesis, more proteins, with greater fold changes, were differentially expressed in response to CLas in adults when compared with nymphs, including insect proteins involved in bacterial adhesion and immunity. Compared with nymphs, adult insects had a higher titre of CLas and the bacterial endosymbionts Wolbachia, Profftella and Carsonella. All Wolbachia and Profftella proteins differentially expressed between nymphs and adults are upregulated in adults, while most differentially expressed Carsonella proteins are upregulated in nymphs. Discovery of protein interaction networks has broad applicability to the study of host–microbe relationships. Using protein interaction reporter technology, a D. citri haemocyanin protein highly upregulated in response to CLas was found to physically interact with the CLas coenzyme A (CoA) biosynthesis enzyme phosphopantothenoylcysteine synthetase/decarboxylase. CLas pantothenate kinase, which catalyses the rate-limiting step of CoA biosynthesis, was found to interact with a D. citri myosin protein. Two Carsonella enzymes involved in histidine and tryptophan biosynthesis were found to physically interact with D. citri proteins. These co-evolved protein interaction networks at the host–microbe interface are highly specific targets for controlling the insect vector responsible for the spread of citrus greening. PMID:28386418

Weighted networks as randomly reinforced urn processes

NASA Astrophysics Data System (ADS)

Caldarelli, Guido; Chessa, Alessandro; Crimaldi, Irene; Pammolli, Fabio

2013-02-01

We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights are determined by a reinforcement mechanism. We develop a statistical test and a procedure based on it to study the evolution of networks over time, detecting the “dominance” of some edges with respect to the others and then assessing if a given instance of the network is taken at its steady state or not. Distance from the steady state can be considered as a measure of the relevance of the observed properties of the network. Our results are quite general, in the sense that they are not based on a particular probability distribution or functional form of the random weights. Moreover, the proposed tool can be applied also to dense networks, which have received little attention by the network community so far, since they are often problematic. We apply our procedure in the context of the International Trade Network, determining a core of “dominant edges.”

Effects of behavioral patterns and network topology structures on Parrondo’s paradox

PubMed Central

Ye, Ye; Cheong, Kang Hao; Cen, Yu-wan; Xie, Neng-gang

2016-01-01

A multi-agent Parrondo’s model based on complex networks is used in the current study. For Parrondo’s game A, the individual interaction can be categorized into five types of behavioral patterns: the Matthew effect, harmony, cooperation, poor-competition-rich-cooperation and a random mode. The parameter space of Parrondo’s paradox pertaining to each behavioral pattern, and the gradual change of the parameter space from a two-dimensional lattice to a random network and from a random network to a scale-free network was analyzed. The simulation results suggest that the size of the region of the parameter space that elicits Parrondo’s paradox is positively correlated with the heterogeneity of the degree distribution of the network. For two distinct sets of probability parameters, the microcosmic reasons underlying the occurrence of the paradox under the scale-free network are elaborated. Common interaction mechanisms of the asymmetric structure of game B, behavioral patterns and network topology are also revealed. PMID:27845430

Effects of behavioral patterns and network topology structures on Parrondo’s paradox

NASA Astrophysics Data System (ADS)

Ye, Ye; Cheong, Kang Hao; Cen, Yu-Wan; Xie, Neng-Gang

2016-11-01

A multi-agent Parrondo’s model based on complex networks is used in the current study. For Parrondo’s game A, the individual interaction can be categorized into five types of behavioral patterns: the Matthew effect, harmony, cooperation, poor-competition-rich-cooperation and a random mode. The parameter space of Parrondo’s paradox pertaining to each behavioral pattern, and the gradual change of the parameter space from a two-dimensional lattice to a random network and from a random network to a scale-free network was analyzed. The simulation results suggest that the size of the region of the parameter space that elicits Parrondo’s paradox is positively correlated with the heterogeneity of the degree distribution of the network. For two distinct sets of probability parameters, the microcosmic reasons underlying the occurrence of the paradox under the scale-free network are elaborated. Common interaction mechanisms of the asymmetric structure of game B, behavioral patterns and network topology are also revealed.

Educational network comparative analysis of small groups: Short- and long-term communications

NASA Astrophysics Data System (ADS)

Berg, D. B.; Zvereva, O. M.; Nazarova, Yu. Yu.; Chepurov, E. G.; Kokovin, A. V.; Ranyuk, S. V.

2017-11-01

The present study is devoted to the discussion of small group communication network structures. These communications were observed in student groups, where actors were united with a regular educational activity. The comparative analysis was carried out for networks of short-term (1 hour) and long-term (4 weeks) communications, it was based on seven structural parameters, and consisted of two stages. At the first stage, differences between the network graphs were examined, and the random corresponding Bernoulli graphs were built. At the second stage, revealed differences were compared. Calculations were performed using UCINET software framework. It was found out that networks of long-term and short-term communications are quite different: the structure of a short-term communication network is close to a random one, whereas the most of long-term communication network parameters differ from the corresponding random ones by more than 30%. This difference can be explained by strong "noisiness" of a short-term communication network, and the lack of social in it.

Modeling of synchronization behavior of bursting neurons at nonlinearly coupled dynamical networks.

PubMed

Çakir, Yüksel

2016-01-01

Synchronization behaviors of bursting neurons coupled through electrical and dynamic chemical synapses are investigated. The Izhikevich model is used with random and small world network of bursting neurons. Various currents which consist of diffusive electrical and time-delayed dynamic chemical synapses are used in the simulations to investigate the influences of synaptic currents and couplings on synchronization behavior of bursting neurons. The effects of parameters, such as time delay, inhibitory synaptic strengths, and decay time on synchronization behavior are investigated. It is observed that in random networks with no delay, bursting synchrony is established with the electrical synapse alone, single spiking synchrony is observed with hybrid coupling. In small world network with no delay, periodic bursting behavior with multiple spikes is observed when only chemical and only electrical synapse exist. Single-spike and multiple-spike bursting are established with hybrid couplings. A decrease in the synchronization measure is observed with zero time delay, as the decay time is increased in random network. For synaptic delays which are above active phase period, synchronization measure increases with an increase in synaptic strength and time delay in small world network. However, in random network, it increases with only an increase in synaptic strength.

A new neural network model for solving random interval linear programming problems.

PubMed

Arjmandzadeh, Ziba; Safi, Mohammadreza; Nazemi, Alireza

2017-05-01

This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem. Several illustrative examples are solved in support of this technique. Copyright © 2017 Elsevier Ltd. All rights reserved.

Exploring activity-driven network with biased walks

NASA Astrophysics Data System (ADS)

Wang, Yan; Wu, Ding Juan; Lv, Fang; Su, Meng Long

We investigate the concurrent dynamics of biased random walks and the activity-driven network, where the preferential transition probability is in terms of the edge-weighting parameter. We also obtain the analytical expressions for stationary distribution and the coverage function in directed and undirected networks, all of which depend on the weight parameter. Appropriately adjusting this parameter, more effective search strategy can be obtained when compared with the unbiased random walk, whether in directed or undirected networks. Since network weights play a significant role in the diffusion process.

Bayesian network meta-analysis for cluster randomized trials with binary outcomes.

PubMed

Uhlmann, Lorenz; Jensen, Katrin; Kieser, Meinhard

2017-06-01

Network meta-analysis is becoming a common approach to combine direct and indirect comparisons of several treatment arms. In recent research, there have been various developments and extensions of the standard methodology. Simultaneously, cluster randomized trials are experiencing an increased popularity, especially in the field of health services research, where, for example, medical practices are the units of randomization but the outcome is measured at the patient level. Combination of the results of cluster randomized trials is challenging. In this tutorial, we examine and compare different approaches for the incorporation of cluster randomized trials in a (network) meta-analysis. Furthermore, we provide practical insight on the implementation of the models. In simulation studies, it is shown that some of the examined approaches lead to unsatisfying results. However, there are alternatives which are suitable to combine cluster randomized trials in a network meta-analysis as they are unbiased and reach accurate coverage rates. In conclusion, the methodology can be extended in such a way that an adequate inclusion of the results obtained in cluster randomized trials becomes feasible. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Detecting phase transitions in a neural network and its application to classification of syndromes in traditional Chinese medicine

NASA Astrophysics Data System (ADS)

Chen, J.; Xi, G.; Wang, W.

2008-02-01

Detecting phase transitions in neural networks (determined or random) presents a challenging subject for phase transitions play a key role in human brain activity. In this paper, we detect numerically phase transitions in two types of random neural network(RNN) under proper parameters.

Two betweenness centrality measures based on Randomized Shortest Paths

PubMed Central

Kivimäki, Ilkka; Lebichot, Bertrand; Saramäki, Jari; Saerens, Marco

2016-01-01

This paper introduces two new closely related betweenness centrality measures based on the Randomized Shortest Paths (RSP) framework, which fill a gap between traditional network centrality measures based on shortest paths and more recent methods considering random walks or current flows. The framework defines Boltzmann probability distributions over paths of the network which focus on the shortest paths, but also take into account longer paths depending on an inverse temperature parameter. RSP’s have previously proven to be useful in defining distance measures on networks. In this work we study their utility in quantifying the importance of the nodes of a network. The proposed RSP betweenness centralities combine, in an optimal way, the ideas of using the shortest and purely random paths for analysing the roles of network nodes, avoiding issues involving these two paradigms. We present the derivations of these measures and how they can be computed in an efficient way. In addition, we show with real world examples the potential of the RSP betweenness centralities in identifying interesting nodes of a network that more traditional methods might fail to notice. PMID:26838176

Listening to the Noise: Random Fluctuations Reveal Gene Network Parameters

NASA Astrophysics Data System (ADS)

Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

2010-03-01

The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant prototypical differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, single molecule fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.

ECTOMYCORRHIZAL DIVERSITY IN A LOBLOLLY PINE (PINUS TAEDA L.) GENETICS PLANTATION: INFLUENCE OF FERTILIZATION

EPA Science Inventory

Loblolly pine (Pinus taeda L.) Has co-evolved a high dependency on ectomycorrhizal (ECM) associations most likely because its natural range includes soils of varying moisture that are P- and/or N-deficient. Because of its wide geographic distrubition, we would expect its roots t...

The Complexity of Care

ERIC Educational Resources Information Center

Collins, Steve; Ting, Hermia

2014-01-01

The profession of teaching is unique because of the extent to which a teacher becomes involved in the lives of their "clients". The level of care required to support students well can be intense, confusing, and overwhelming. Relationships co-evolve within an ever-changing process and care is considered an essential aspect of complex relationships…

Social Labs in Universities: Innovation and Impact in Medialab UGR

ERIC Educational Resources Information Center

Romero-Frías, Esteban; Robinson-García, Nicolás

2017-01-01

Social laboratories, defined as experimental spaces for co-creation, have recently become the main centers of innovation. Medialabs are experimental laboratories of technologies and communication media which have co-evolved along with the digital society into mediation laboratories of citizen experimentation, observing a confluence of both models.…

Fathers' and Mothers' Parenting Predicting and Responding to Adolescent Sexual Risk Behaviors

ERIC Educational Resources Information Center

Coley, Rebekah Levine; Votruba-Drzal, Elizabeth; Schindler, Holly S.

2009-01-01

Transactional models of problem behavior argue that less effective parenting and adolescent problem behaviors coevolve, exerting bidirectional influences. This article extends such models by analyzing growth trajectories of sexual risk behaviors and parenting processes among 3,206 adolescents (aged 13-18) and their residential parents. Within…

Analysis of co-evolving genes in campylobacter jejuni and C. coli

USDA-ARS?s Scientific Manuscript database

Background: The population structure of Campylobacter has been frequently studied by MLST, for which fragments of housekeeping genes are compared. We wished to determine if the used MLST genes are representative of the complete genome. Methods: A set of 1029 core gene families (CGF) was identifie...

Diffusion in random networks

DOE PAGES

Zhang, Duan Z.; Padrino, Juan C.

2017-06-01

The ensemble averaging technique is applied to model mass transport by diffusion in random networks. The system consists of an ensemble of random networks, where each network is made of pockets connected by tortuous channels. Inside a channel, fluid transport is assumed to be governed by the one-dimensional diffusion equation. Mass balance leads to an integro-differential equation for the pocket mass density. The so-called dual-porosity model is found to be equivalent to the leading order approximation of the integration kernel when the diffusion time scale inside the channels is small compared to the macroscopic time scale. As a test problem,more » we consider the one-dimensional mass diffusion in a semi-infinite domain. Because of the required time to establish the linear concentration profile inside a channel, for early times the similarity variable is xt $-$1/4 rather than xt $-$1/2 as in the traditional theory. We found this early time similarity can be explained by random walk theory through the network.« less

Percolation and Reinforcement on Complex Networks

NASA Astrophysics Data System (ADS)

Yuan, Xin

Complex networks appear in almost every aspect of our daily life and are widely studied in the fields of physics, mathematics, finance, biology and computer science. This work utilizes percolation theory in statistical physics to explore the percolation properties of complex networks and develops a reinforcement scheme on improving network resilience. This dissertation covers two major parts of my Ph.D. research on complex networks: i) probe--in the context of both traditional percolation and k-core percolation--the resilience of complex networks with tunable degree distributions or directed dependency links under random, localized or targeted attacks; ii) develop and propose a reinforcement scheme to eradicate catastrophic collapses that occur very often in interdependent networks. We first use generating function and probabilistic methods to obtain analytical solutions to percolation properties of interest, such as the giant component size and the critical occupation probability. We study uncorrelated random networks with Poisson, bi-Poisson, power-law, and Kronecker-delta degree distributions and construct those networks which are based on the configuration model. The computer simulation results show remarkable agreement with theoretical predictions. We discover an increase of network robustness as the degree distribution broadens and a decrease of network robustness as directed dependency links come into play under random attacks. We also find that targeted attacks exert the biggest damage to the structure of both single and interdependent networks in k-core percolation. To strengthen the resilience of interdependent networks, we develop and propose a reinforcement strategy and obtain the critical amount of reinforced nodes analytically for interdependent Erdḧs-Renyi networks and numerically for scale-free and for random regular networks. Our mechanism leads to improvement of network stability of the West U.S. power grid. This dissertation provides us with a deeper understanding of the effects of structural features on network stability and fresher insights into designing resilient interdependent infrastructure networks.

Critical exponents for diluted resistor networks

NASA Astrophysics Data System (ADS)

Stenull, O.; Janssen, H. K.; Oerding, K.

1999-05-01

An approach by Stephen [Phys. Rev. B 17, 4444 (1978)] is used to investigate the critical properties of randomly diluted resistor networks near the percolation threshold by means of renormalized field theory. We reformulate an existing field theory by Harris and Lubensky [Phys. Rev. B 35, 6964 (1987)]. By a decomposition of the principal Feynman diagrams, we obtain diagrams which again can be interpreted as resistor networks. This interpretation provides for an alternative way of evaluating the Feynman diagrams for random resistor networks. We calculate the resistance crossover exponent φ up to second order in ɛ=6-d, where d is the spatial dimension. Our result φ=1+ɛ/42+4ɛ2/3087 verifies a previous calculation by Lubensky and Wang, which itself was based on the Potts-model formulation of the random resistor network.

A method for validating Rent's rule for technological and biological networks.

PubMed

Alcalde Cuesta, Fernando; González Sequeiros, Pablo; Lozano Rojo, Álvaro

2017-07-14

Rent's rule is empirical power law introduced in an effort to describe and optimize the wiring complexity of computer logic graphs. It is known that brain and neuronal networks also obey Rent's rule, which is consistent with the idea that wiring costs play a fundamental role in brain evolution and development. Here we propose a method to validate this power law for a certain range of network partitions. This method is based on the bifurcation phenomenon that appears when the network is subjected to random alterations preserving its degree distribution. It has been tested on a set of VLSI circuits and real networks, including biological and technological ones. We also analyzed the effect of different types of random alterations on the Rentian scaling in order to test the influence of the degree distribution. There are network architectures quite sensitive to these randomization procedures with significant increases in the values of the Rent exponents.

Network Dynamics of Innovation Processes.

PubMed

Iacopini, Iacopo; Milojević, Staša; Latora, Vito

2018-01-26

We introduce a model for the emergence of innovations, in which cognitive processes are described as random walks on the network of links among ideas or concepts, and an innovation corresponds to the first visit of a node. The transition matrix of the random walk depends on the network weights, while in turn the weight of an edge is reinforced by the passage of a walker. The presence of the network naturally accounts for the mechanism of the "adjacent possible," and the model reproduces both the rate at which novelties emerge and the correlations among them observed empirically. We show this by using synthetic networks and by studying real data sets on the growth of knowledge in different scientific disciplines. Edge-reinforced random walks on complex topologies offer a new modeling framework for the dynamics of correlated novelties and are another example of coevolution of processes and networks.

Network Dynamics of Innovation Processes

NASA Astrophysics Data System (ADS)

Iacopini, Iacopo; Milojević, Staša; Latora, Vito

2018-01-01

We introduce a model for the emergence of innovations, in which cognitive processes are described as random walks on the network of links among ideas or concepts, and an innovation corresponds to the first visit of a node. The transition matrix of the random walk depends on the network weights, while in turn the weight of an edge is reinforced by the passage of a walker. The presence of the network naturally accounts for the mechanism of the "adjacent possible," and the model reproduces both the rate at which novelties emerge and the correlations among them observed empirically. We show this by using synthetic networks and by studying real data sets on the growth of knowledge in different scientific disciplines. Edge-reinforced random walks on complex topologies offer a new modeling framework for the dynamics of correlated novelties and are another example of coevolution of processes and networks.

Random Bits Forest: a Strong Classifier/Regressor for Big Data

NASA Astrophysics Data System (ADS)

Wang, Yi; Li, Yi; Pu, Weilin; Wen, Kathryn; Shugart, Yin Yao; Xiong, Momiao; Jin, Li

2016-07-01

Efficiency, memory consumption, and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), a classification and regression algorithm that integrates neural networks (for depth), boosting (for width), and random forests (for prediction accuracy). Through a gradient boosting scheme, it first generates and selects ~10,000 small, 3-layer random neural networks. These networks are then fed into a modified random forest algorithm to obtain predictions. Testing with datasets from the UCI (University of California, Irvine) Machine Learning Repository shows that RBF outperforms other popular methods in both accuracy and robustness, especially with large datasets (N > 1000). The algorithm also performed highly in testing with an independent data set, a real psoriasis genome-wide association study (GWAS).

How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?

PubMed

Grabska-Barwińska, Agnieszka; Latham, Peter E

2014-06-01

We use mean field techniques to compute the distribution of excitatory and inhibitory firing rates in large networks of randomly connected spiking quadratic integrate and fire neurons. These techniques are based on the assumption that activity is asynchronous and Poisson. For most parameter settings these assumptions are strongly violated; nevertheless, so long as the networks are not too synchronous, we find good agreement between mean field prediction and network simulations. Thus, much of the intuition developed for randomly connected networks in the asynchronous regime applies to mildly synchronous networks.

Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks.

PubMed

Hsieh, Chung-Ho; Lu, Ruey-Hwa; Lee, Nai-Hsin; Chiu, Wen-Ta; Hsu, Min-Huei; Li, Yu-Chuan Jack

2011-01-01

Diagnosing acute appendicitis clinically is still difficult. We developed random forests, support vector machines, and artificial neural network models to diagnose acute appendicitis. Between January 2006 and December 2008, patients who had a consultation session with surgeons for suspected acute appendicitis were enrolled. Seventy-five percent of the data set was used to construct models including random forest, support vector machines, artificial neural networks, and logistic regression. Twenty-five percent of the data set was withheld to evaluate model performance. The area under the receiver operating characteristic curve (AUC) was used to evaluate performance, which was compared with that of the Alvarado score. Data from a total of 180 patients were collected, 135 used for training and 45 for testing. The mean age of patients was 39.4 years (range, 16-85). Final diagnosis revealed 115 patients with and 65 without appendicitis. The AUC of random forest, support vector machines, artificial neural networks, logistic regression, and Alvarado was 0.98, 0.96, 0.91, 0.87, and 0.77, respectively. The sensitivity, specificity, positive, and negative predictive values of random forest were 94%, 100%, 100%, and 87%, respectively. Random forest performed better than artificial neural networks, logistic regression, and Alvarado. We demonstrated that random forest can predict acute appendicitis with good accuracy and, deployed appropriately, can be an effective tool in clinical decision making. Copyright © 2011 Mosby, Inc. All rights reserved.

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.

Probabilistic generation of random networks taking into account information on motifs occurrence.

PubMed

Bois, Frederic Y; Gayraud, Ghislaine

2015-01-01

Because of the huge number of graphs possible even with a small number of nodes, inference on network structure is known to be a challenging problem. Generating large random directed graphs with prescribed probabilities of occurrences of some meaningful patterns (motifs) is also difficult. We show how to generate such random graphs according to a formal probabilistic representation, using fast Markov chain Monte Carlo methods to sample them. As an illustration, we generate realistic graphs with several hundred nodes mimicking a gene transcription interaction network in Escherichia coli.

Probabilistic Generation of Random Networks Taking into Account Information on Motifs Occurrence

PubMed Central

Bois, Frederic Y.

2015-01-01

Abstract Because of the huge number of graphs possible even with a small number of nodes, inference on network structure is known to be a challenging problem. Generating large random directed graphs with prescribed probabilities of occurrences of some meaningful patterns (motifs) is also difficult. We show how to generate such random graphs according to a formal probabilistic representation, using fast Markov chain Monte Carlo methods to sample them. As an illustration, we generate realistic graphs with several hundred nodes mimicking a gene transcription interaction network in Escherichia coli. PMID:25493547

A Dynamic Bayesian Network Model for the Production and Inventory Control

NASA Astrophysics Data System (ADS)

Shin, Ji-Sun; Takazaki, Noriyuki; Lee, Tae-Hong; Kim, Jin-Il; Lee, Hee-Hyol

In general, the production quantities and delivered goods are changed randomly and then the total stock is also changed randomly. This paper deals with the production and inventory control using the Dynamic Bayesian Network. Bayesian Network is a probabilistic model which represents the qualitative dependence between two or more random variables by the graph structure, and indicates the quantitative relations between individual variables by the conditional probability. The probabilistic distribution of the total stock is calculated through the propagation of the probability on the network. Moreover, an adjusting rule of the production quantities to maintain the probability of a lower limit and a ceiling of the total stock to certain values is shown.

Mass media influence spreading in social networks with community structure

NASA Astrophysics Data System (ADS)

Candia, Julián; Mazzitello, Karina I.

2008-07-01

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

Cascade phenomenon against subsequent failures in complex networks

NASA Astrophysics Data System (ADS)

Jiang, Zhong-Yuan; Liu, Zhi-Quan; He, Xuan; Ma, Jian-Feng

2018-06-01

Cascade phenomenon may lead to catastrophic disasters which extremely imperil the network safety or security in various complex systems such as communication networks, power grids, social networks and so on. In some flow-based networks, the load of failed nodes can be redistributed locally to their neighboring nodes to maximally preserve the traffic oscillations or large-scale cascading failures. However, in such local flow redistribution model, a small set of key nodes attacked subsequently can result in network collapse. Then it is a critical problem to effectively find the set of key nodes in the network. To our best knowledge, this work is the first to study this problem comprehensively. We first introduce the extra capacity for every node to put up with flow fluctuations from neighbors, and two extra capacity distributions including degree based distribution and average distribution are employed. Four heuristic key nodes discovering methods including High-Degree-First (HDF), Low-Degree-First (LDF), Random and Greedy Algorithms (GA) are presented. Extensive simulations are realized in both scale-free networks and random networks. The results show that the greedy algorithm can efficiently find the set of key nodes in both scale-free and random networks. Our work studies network robustness against cascading failures from a very novel perspective, and methods and results are very useful for network robustness evaluations and protections.

Coevolutionary dynamics in large, but finite populations

NASA Astrophysics Data System (ADS)

Traulsen, Arne; Claussen, Jens Christian; Hauert, Christoph

2006-07-01

Coevolving and competing species or game-theoretic strategies exhibit rich and complex dynamics for which a general theoretical framework based on finite populations is still lacking. Recently, an explicit mean-field description in the form of a Fokker-Planck equation was derived for frequency-dependent selection with two strategies in finite populations based on microscopic processes [A. Traulsen, J. C. Claussen, and C. Hauert, Phys. Rev. Lett. 95, 238701 (2005)]. Here we generalize this approach in a twofold way: First, we extend the framework to an arbitrary number of strategies and second, we allow for mutations in the evolutionary process. The deterministic limit of infinite population size of the frequency-dependent Moran process yields the adjusted replicator-mutator equation, which describes the combined effect of selection and mutation. For finite populations, we provide an extension taking random drift into account. In the limit of neutral selection, i.e., whenever the process is determined by random drift and mutations, the stationary strategy distribution is derived. This distribution forms the background for the coevolutionary process. In particular, a critical mutation rate uc is obtained separating two scenarios: above uc the population predominantly consists of a mixture of strategies whereas below uc the population tends to be in homogeneous states. For one of the fundamental problems in evolutionary biology, the evolution of cooperation under Darwinian selection, we demonstrate that the analytical framework provides excellent approximations to individual based simulations even for rather small population sizes. This approach complements simulation results and provides a deeper, systematic understanding of coevolutionary dynamics.

Dynamics of comb-of-comb-network polymers in random layered flows

NASA Astrophysics Data System (ADS)

Katyal, Divya; Kant, Rama

2016-12-01

We analyze the dynamics of comb-of-comb-network polymers in the presence of external random flows. The dynamics of such structures is evaluated through relevant physical quantities, viz., average square displacement (ASD) and the velocity autocorrelation function (VACF). We focus on comparing the dynamics of the comb-of-comb network with the linear polymer. The present work displays an anomalous diffusive behavior of this flexible network in the random layered flows. The effect of the polymer topology on the dynamics is analyzed by varying the number of generations and branch lengths in these networks. In addition, we investigate the influence of external flow on the dynamics by varying flow parameters, like the flow exponent α and flow strength Wα. Our analysis highlights two anomalous power-law regimes, viz., subdiffusive (intermediate-time polymer stretching and flow-induced diffusion) and superdiffusive (long-time flow-induced diffusion). The anomalous long-time dynamics is governed by the temporal exponent ν of ASD, viz., ν =2 -α /2 . Compared to a linear polymer, the comb-of-comb network shows a shorter crossover time (from the subdiffusive to superdiffusive regime) but a reduced magnitude of ASD. Our theory displays an anomalous VACF in the random layered flows that scales as t-α /2. We show that the network with greater total mass moves faster.

Optimal Quantum Spatial Search on Random Temporal Networks

NASA Astrophysics Data System (ADS)

Chakraborty, Shantanav; Novo, Leonardo; Di Giorgio, Serena; Omar, Yasser

2017-12-01

To investigate the performance of quantum information tasks on networks whose topology changes in time, we study the spatial search algorithm by continuous time quantum walk to find a marked node on a random temporal network. We consider a network of n nodes constituted by a time-ordered sequence of Erdös-Rényi random graphs G (n ,p ), where p is the probability that any two given nodes are connected: After every time interval τ , a new graph G (n ,p ) replaces the previous one. We prove analytically that, for any given p , there is always a range of values of τ for which the running time of the algorithm is optimal, i.e., O (√{n }), even when search on the individual static graphs constituting the temporal network is suboptimal. On the other hand, there are regimes of τ where the algorithm is suboptimal even when each of the underlying static graphs are sufficiently connected to perform optimal search on them. From this first study of quantum spatial search on a time-dependent network, it emerges that the nontrivial interplay between temporality and connectivity is key to the algorithmic performance. Moreover, our work can be extended to establish high-fidelity qubit transfer between any two nodes of the network. Overall, our findings show that one can exploit temporality to achieve optimal quantum information tasks on dynamical random networks.

Sustained Activity in Hierarchical Modular Neural Networks: Self-Organized Criticality and Oscillations

PubMed Central

Wang, Sheng-Jun; Hilgetag, Claus C.; Zhou, Changsong

2010-01-01

Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information processing. PMID:21852971

Failure and recovery in dynamical networks.

PubMed

Böttcher, L; Luković, M; Nagler, J; Havlin, S; Herrmann, H J

2017-02-03

Failure, damage spread and recovery crucially underlie many spatially embedded networked systems ranging from transportation structures to the human body. Here we study the interplay between spontaneous damage, induced failure and recovery in both embedded and non-embedded networks. In our model the network's components follow three realistic processes that capture these features: (i) spontaneous failure of a component independent of the neighborhood (internal failure), (ii) failure induced by failed neighboring nodes (external failure) and (iii) spontaneous recovery of a component. We identify a metastable domain in the global network phase diagram spanned by the model's control parameters where dramatic hysteresis effects and random switching between two coexisting states are observed. This dynamics depends on the characteristic link length of the embedded system. For the Euclidean lattice in particular, hysteresis and switching only occur in an extremely narrow region of the parameter space compared to random networks. We develop a unifying theory which links the dynamics of our model to contact processes. Our unifying framework may help to better understand controllability in spatially embedded and random networks where spontaneous recovery of components can mitigate spontaneous failure and damage spread in dynamical networks.

Group field theory and tensor networks: towards a Ryu–Takayanagi formula in full quantum gravity

NASA Astrophysics Data System (ADS)

Chirco, Goffredo; Oriti, Daniele; Zhang, Mingyi

2018-06-01

We establish a dictionary between group field theory (thus, spin networks and random tensors) states and generalized random tensor networks. Then, we use this dictionary to compute the Rényi entropy of such states and recover the Ryu–Takayanagi formula, in two different cases corresponding to two different truncations/approximations, suggested by the established correspondence.

Random Time Identity Based Firewall In Mobile Ad hoc Networks

NASA Astrophysics Data System (ADS)

Suman, Patel, R. B.; Singh, Parvinder

2010-11-01

A mobile ad hoc network (MANET) is a self-organizing network of mobile routers and associated hosts connected by wireless links. MANETs are highly flexible and adaptable but at the same time are highly prone to security risks due to the open medium, dynamically changing network topology, cooperative algorithms, and lack of centralized control. Firewall is an effective means of protecting a local network from network-based security threats and forms a key component in MANET security architecture. This paper presents a review of firewall implementation techniques in MANETs and their relative merits and demerits. A new approach is proposed to select MANET nodes at random for firewall implementation. This approach randomly select a new node as firewall after fixed time and based on critical value of certain parameters like power backup. This approach effectively balances power and resource utilization of entire MANET because responsibility of implementing firewall is equally shared among all the nodes. At the same time it ensures improved security for MANETs from outside attacks as intruder will not be able to find out the entry point in MANET due to the random selection of nodes for firewall implementation.

Emerald ash borer, black ash, and Native American basketmaking

Treesearch

Therese M. Poland; Marla R. Emery; Tina Ciaramitaro; Ed Pigeon; Angie Pigeon

2017-01-01

Native cultures coevolved with the forests of the Great Lakes region following the last ice age. Plentiful water, abundant game, and fertile soil supported fishing, hunting, and gathering, as well as subsistence agriculture. Lakes and tributaries facilitated transportation by canoe and trade among tribes. Native Americans developed a semi-nomadic lifestyle, moving...

Tree physiology and bark beetles

Treesearch

Michael G. Ryan; Gerard Sapes; Anna Sala; Sharon Hood

2015-01-01

Irruptive bark beetles usually co-occur with their co-evolved tree hosts at very low (endemic) population densities. However, recent droughts and higher temperatures have promoted widespread tree mortality with consequences for forest carbon, fire and ecosystem services (Kurz et al., 2008; Raffa et al., 2008; Jenkins et al., 2012). In this issue of New Phytologist,...

Identity and Intimacy during Adolescence: Connections among Identity Styles, Romantic Attachment and Identity Commitment

ERIC Educational Resources Information Center

Kerpelman, Jennifer L.; Pittman, Joe F.; Cadely, Hans Saint-Eloi; Tuggle, Felicia J.; Harrell-Levy, Marinda K.; Adler-Baeder, Francesca M.

2012-01-01

Integration of adult attachment and psychosocial development theories suggests that adolescence is a time when capacities for romantic intimacy and identity formation are co-evolving. The current study addressed direct, indirect and moderated associations among identity and romantic attachment constructs with a diverse sample of 2178 middle…

Inflammatory phenotypes in the intestine of poultry: Not all inflammation is created equally

USDA-ARS?s Scientific Manuscript database

The intestinal tract harbors a diverse community of microbes that have co-evolved with the host immune system. Although many of these microbes execute functions that are critical for host physiology, the host immune system must control the microbial community so that the dynamics of this interdepen...

Honey bee gut dysbiosis: A novel context for disease ecology

USDA-ARS?s Scientific Manuscript database

A biofilm in the ileum of the honey bee has become a hot-spot of recent research. Highly host co-evolved, hindgut biofilm structure consists of six species that alter in relative abundance over the life of the adult worker, showing parallels with age-related senescence in Drosophila. Induced shifts ...

Ideology and Interaction: Debating Determinisms in Literacy Studies

ERIC Educational Resources Information Center

Collin, Ross; Street, Brian V.

2014-01-01

In this exchange, Street and Collin debate the merits of the interaction model of literacy that Collin outlined in a recent issue of Reading Research Quarterly. Built as a complement and a counter to Street's ideological model of literacy, Collin's interaction model defines literacies as technologies that coevolve with sociocultural…

Optimists' Creed: Brave New Cyberlearning, Evolving Utopias (Circa 2041)

ERIC Educational Resources Information Center

Burleson, Winslow; Lewis, Armanda

2016-01-01

This essay imagines the role that artificial intelligence innovations play in the integrated living, learning and research environments of 2041. Here, in 2041, in the context of increasingly complex wicked challenges, whose solutions by their very nature continue to evade even the most capable experts, society and technology have co-evolved to…

Spatial heterogeneity lowers rather than increases host-parasite specialization.

PubMed

Hesse, E; Best, A; Boots, M; Hall, A R; Buckling, A

2015-09-01

Abiotic environmental heterogeneity can promote the evolution of diverse resource specialists, which in turn may increase the degree of host-parasite specialization. We coevolved Pseudomonas fluorescens and lytic phage ϕ2 in spatially structured populations, each consisting of two interconnected subpopulations evolving in the same or different nutrient media (homogeneous and heterogeneous environments, respectively). Counter to the normal expectation, host-parasite specialization was significantly lower in heterogeneous compared with homogeneous environments. This result could not be explained by dispersal homogenizing populations, as this would have resulted in the heterogeneous treatments having levels of specialization equal to or greater than that of the homogeneous environments. We argue that selection for costly generalists is greatest when the coevolving species are exposed to diverse environmental conditions and that this can provide an explanation for our results. A simple coevolutionary model of this process suggests that this can be a general mechanism by which environmental heterogeneity can reduce rather than increase host-parasite specialization. © 2015 The Authors. J. EVOL. BIOL. Journal of Evolutionary Biology Published by John Wiley & Sons Ltd on Behalf of European Society for Evolutionary Biology.

Reciprocal selection causes a coevolutionary arms race between crossbills and lodgepole pine.

PubMed

Benkman, Craig W; Parchman, Thomas L; Favis, Amanda; Siepielski, Adam M

2003-08-01

Few studies have shown both reciprocal selection and reciprocal adaptations for a coevolving system in the wild. The goal of our study was to determine whether the patterns of selection on Rocky Mountain lodgepole pine (Pinus contorta spp. latifolia) and red crossbills (Loxia curvirostra complex) were concordant with earlier published evidence of reciprocal adaptations in lodgepole pine and crossbills on isolated mountain ranges in the absence of red squirrels (Tamiasciurus hudsonicus). We found that selection (directional) by crossbills on lodgepole pine where Tamiasciurus are absent was divergent from the selection (directional) exerted by Tamiasciurus on lodgepole pine. This resulted in divergent selection between areas with and without Tamiasciurus that was congruent with the geographic patterns of cone variation. In the South Hills, Idaho, where Tamiasciurus are absent and red crossbills are thought to be coevolving with lodgepole pine, crossbills experienced stabilizing selection on bill size, with cone structure as the agent of selection. These results show that crossbills and lodgepole pine exhibit reciprocal adaptations in response to reciprocal selection, and they provide insight into the traits mediating and responding to selection in a coevolutionary arms race.

Plant-associated methylobacteria as co-evolved phytosymbionts: a hypothesis.

PubMed

Kutschera, Ulrich

2007-03-01

Due to their wall-associated pectin metabolism, growing plant cells emit significant amounts of the one-carbon alcohol methanol. Pink-pigmented microbes of the genus Methylobacterium that colonize the surfaces of leaves (epiphytes) are capable of growth on this volatile C1-compound as sole source of carbon and energy. In this article the results of experiments with germ-free (gnotobiotic) sporophytes of angiosperms (sunflower, maize) and gametophytes of bryophytes (a moss and two liverwort species) are summarized. The data show that methylobacteria do not stimulate the growth of these angiosperms, but organ development in moss protonemata and in thalli of liverworts is considerably enhanced. Since methylobacteria produce and secrete cytokinins and auxin, a model of plant-microbe-interaction (symbiosis) is proposed in which the methanol-consuming bacteria are viewed as coevolved partners of the gametophyte that determine its growth, survival and reproduction (fitness). This symbiosis is restricted to the haploid cells of moisture-dependent "living fossil" plants; it does not apply to the diploid sporophytes of higher embryophytes, which are fully adapted to life on land and apparently produce sufficient amounts of endogenous phytohormones.

Coevolution between Male and Female Genitalia in the Drosophila melanogaster Species Subgroup

PubMed Central

Yassin, Amir; Orgogozo, Virginie

2013-01-01

In contrast to male genitalia that typically exhibit patterns of rapid and divergent evolution among internally fertilizing animals, female genitalia have been less well studied and are generally thought to evolve slowly among closely-related species. As a result, few cases of male-female genital coevolution have been documented. In Drosophila, female copulatory structures have been claimed to be mostly invariant compared to male structures. Here, we re-examined male and female genitalia in the nine species of the D. melanogaster subgroup. We describe several new species-specific female genital structures that appear to coevolve with male genital structures, and provide evidence that the coevolving structures contact each other during copulation. Several female structures might be defensive shields against apparently harmful male structures, such as cercal teeth, phallic hooks and spines. Evidence for male-female morphological coevolution in Drosophila has previously been shown at the post-copulatory level (e.g., sperm length and sperm storage organ size), and our results provide support for male-female coevolution at the copulatory level. PMID:23451172

Supermassive black holes do not correlate with dark matter haloes of galaxies.

PubMed

Kormendy, John; Bender, Ralf

2011-01-20

Supermassive black holes have been detected in all galaxies that contain bulge components when the galaxies observed were close enough that the searches were feasible. Together with the observation that bigger black holes live in bigger bulges, this has led to the belief that black-hole growth and bulge formation regulate each other. That is, black holes and bulges coevolve. Therefore, reports of a similar correlation between black holes and the dark matter haloes in which visible galaxies are embedded have profound implications. Dark matter is likely to be non-baryonic, so these reports suggest that unknown, exotic physics controls black-hole growth. Here we show, in part on the basis of recent measurements of bulgeless galaxies, that there is almost no correlation between dark matter and parameters that measure black holes unless the galaxy also contains a bulge. We conclude that black holes do not correlate directly with dark matter. They do not correlate with galaxy disks, either. Therefore, black holes coevolve only with bulges. This simplifies the puzzle of their coevolution by focusing attention on purely baryonic processes in the galaxy mergers that make bulges.

The challenges of modelling antibody repertoire dynamics in HIV infection

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

Luo, Shishi; Perelson, Alan S.

Antibody affinity maturation by somatic hypermutation of B-cell immunoglobulin variable region genes has been studied for decades in various model systems using well-defined antigens. While much is known about the molecular details of the process, our understanding of the selective forces that generate affinity maturation are less well developed, particularly in the case of a co-evolving pathogen such as HIV. Despite this gap in understanding, high-throughput antibody sequence data are increasingly being collected to investigate the evolutionary trajectories of antibody lineages in HIV-infected individuals. Here, we review what is known in controlled experimental systems about the mechanisms underlying antibody selectionmore » and compare this to the observed temporal patterns of antibody evolution in HIV infection. In addition, we describe how our current understanding of antibody selection mechanisms leaves questions about antibody dynamics in HIV infection unanswered. Without a mechanistic understanding of antibody selection in the context of a co-evolving viral population, modelling and analysis of antibody sequences in HIV-infected individuals will be limited in their interpretation and predictive ability.« less

How the indirect reciprocity with co-evolving norm and strategy for 2 × 2 prisoner's dilemma game works for emerging cooperation

NASA Astrophysics Data System (ADS)

Tanimoto, Jun; Sagara, Hirokji

2015-11-01

We built a new indirect reciprocity model based on binary image scores, where an agent's strategy and norm co-evolve. The norm, meaning what behavior is evaluated as "good" or "bad," stipulates how image scores of two agents playing a game is altered, which has been presumed to be a fixed value in most previous studies. Also, unlike former studies, our model allows an agent to play with an agent who has a different norm. This point of relaxing the freedom of the model pulls down cooperation level vis-à-vis the case where an agent always plays with another one having same norm. However, it is observed that a rather larger dilemma shows robust cooperation establishing compared with a smaller dilemma, since a norm that punishes a so-called second-order free-rider is prompted. To encourage the evolution of norms to be able to punish second-order free-riders, a society needs a small number of defectors. This is elucidated by the fact that cases with action error are more cooperative than those without action error.

Plant-Associated Methylobacteria as Co-Evolved Phytosymbionts

PubMed Central

2007-01-01

Due to their wall-associated pectin metabolism, growing plant cells emit significant amounts of the one-carbon alcohol methanol. Pink-pigmented microbes of the genus Methylobacterium that colonize the surfaces of leaves (epiphytes) are capable of growth on this volatile C1-compound as sole source of carbon and energy. In this article the results of experiments with germ-free (gnotobiotic) sporophytes of angiosperms (sunflower, maize) and gametophytes of bryophytes (a moss and two liverwort species) are summarized. The data show that methylobacteria do not stimulate the growth of these angiosperms, but organ development in moss protonemata and in thalli of liverworts is considerably enhanced. Since methylobacteria produce and secrete cytokinins and auxin, a model of plant-microbe-interaction (symbiosis) is proposed in which the methanol-consuming bacteria are viewed as coevolved partners of the gametophyte that determine its growth, survival and reproduction (fitness). This symbiosis is restricted to the haploid cells of moisture-dependent “living fossil” plants; it does not apply to the diploid sporophytes of higher embryophytes, which are fully adapted to life on land and apparently produce sufficient amounts of endogenous phytohormones. PMID:19516971

The challenges of modelling antibody repertoire dynamics in HIV infection

DOE PAGES

Luo, Shishi; Perelson, Alan S.

2015-07-20

Antibody affinity maturation by somatic hypermutation of B-cell immunoglobulin variable region genes has been studied for decades in various model systems using well-defined antigens. While much is known about the molecular details of the process, our understanding of the selective forces that generate affinity maturation are less well developed, particularly in the case of a co-evolving pathogen such as HIV. Despite this gap in understanding, high-throughput antibody sequence data are increasingly being collected to investigate the evolutionary trajectories of antibody lineages in HIV-infected individuals. Here, we review what is known in controlled experimental systems about the mechanisms underlying antibody selectionmore » and compare this to the observed temporal patterns of antibody evolution in HIV infection. In addition, we describe how our current understanding of antibody selection mechanisms leaves questions about antibody dynamics in HIV infection unanswered. Without a mechanistic understanding of antibody selection in the context of a co-evolving viral population, modelling and analysis of antibody sequences in HIV-infected individuals will be limited in their interpretation and predictive ability.« less

Flexible embedding of networks

NASA Astrophysics Data System (ADS)

Fernandez-Gracia, Juan; Buckee, Caroline; Onnela, Jukka-Pekka

We introduce a model for embedding one network into another, focusing on the case where network A is much bigger than network B. Nodes from network A are assigned to the nodes in network B using an algorithm where we control the extent of localization of node placement in network B using a single parameter. Starting from an unassigned node in network A, called the source node, we first map this node to a randomly chosen node in network B, called the target node. We then assign the neighbors of the source node to the neighborhood of the target node using a random walk based approach. To assign each neighbor of the source node to one of the nodes in network B, we perform a random walk starting from the target node with stopping probability α. We repeat this process until all nodes in network A have been mapped to the nodes of network B. The simplicity of the model allows us to calculate key quantities of interest in closed form. By varying the parameter α, we are able to produce embeddings from very local (α = 1) to very global (α --> 0). We show how our calculations fit the simulated results, and we apply the model to study how social networks are embedded in geography and how the neurons of C. Elegans are embedded in the surrounding volume.

Unraveling spurious properties of interaction networks with tailored random networks.

PubMed

Bialonski, Stephan; Wendler, Martin; Lehnertz, Klaus

2011-01-01

We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.

Unraveling Spurious Properties of Interaction Networks with Tailored Random Networks

PubMed Central

Bialonski, Stephan; Wendler, Martin; Lehnertz, Klaus

2011-01-01

We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis. PMID:21850239

Will electrical cyber-physical interdependent networks undergo first-order transition under random attacks?

NASA Astrophysics Data System (ADS)

Ji, Xingpei; Wang, Bo; Liu, Dichen; Dong, Zhaoyang; Chen, Guo; Zhu, Zhenshan; Zhu, Xuedong; Wang, Xunting

2016-10-01

Whether the realistic electrical cyber-physical interdependent networks will undergo first-order transition under random failures still remains a question. To reflect the reality of Chinese electrical cyber-physical system, the "partial one-to-one correspondence" interdependent networks model is proposed and the connectivity vulnerabilities of three realistic electrical cyber-physical interdependent networks are analyzed. The simulation results show that due to the service demands of power system the topologies of power grid and its cyber network are highly inter-similar which can effectively avoid the first-order transition. By comparing the vulnerability curves between electrical cyber-physical interdependent networks and its single-layer network, we find that complex network theory is still useful in the vulnerability analysis of electrical cyber-physical interdependent networks.

Macrostructure from Microstructure: Generating Whole Systems from Ego Networks

PubMed Central

Smith, Jeffrey A.

2014-01-01

This paper presents a new simulation method to make global network inference from sampled data. The proposed simulation method takes sampled ego network data and uses Exponential Random Graph Models (ERGM) to reconstruct the features of the true, unknown network. After describing the method, the paper presents two validity checks of the approach: the first uses the 20 largest Add Health networks while the second uses the Sociology Coauthorship network in the 1990's. For each test, I take random ego network samples from the known networks and use my method to make global network inference. I find that my method successfully reproduces the properties of the networks, such as distance and main component size. The results also suggest that simpler, baseline models provide considerably worse estimates for most network properties. I end the paper by discussing the bounds/limitations of ego network sampling. I also discuss possible extensions to the proposed approach. PMID:25339783

The effect of the neural activity on topological properties of growing neural networks.

PubMed

Gafarov, F M; Gafarova, V R

2016-09-01

The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

Road Network State Estimation Using Random Forest Ensemble Learning

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

Hou, Yi; Edara, Praveen; Chang, Yohan

Network-scale travel time prediction not only enables traffic management centers (TMC) to proactively implement traffic management strategies, but also allows travelers make informed decisions about route choices between various origins and destinations. In this paper, a random forest estimator was proposed to predict travel time in a network. The estimator was trained using two years of historical travel time data for a case study network in St. Louis, Missouri. Both temporal and spatial effects were considered in the modeling process. The random forest models predicted travel times accurately during both congested and uncongested traffic conditions. The computational times for themore » models were low, thus useful for real-time traffic management and traveler information applications.« less

HIV-1 protease-substrate coevolution in nelfinavir resistance.

PubMed

Kolli, Madhavi; Ozen, Ayşegül; Kurt-Yilmaz, Nese; Schiffer, Celia A

2014-07-01

Resistance to various human immunodeficiency virus type 1 (HIV-1) protease inhibitors (PIs) challenges the effectiveness of therapies in treating HIV-1-infected individuals and AIDS patients. The virus accumulates mutations within the protease (PR) that render the PIs less potent. Occasionally, Gag sequences also coevolve with mutations at PR cleavage sites contributing to drug resistance. In this study, we investigated the structural basis of coevolution of the p1-p6 cleavage site with the nelfinavir (NFV) resistance D30N/N88D protease mutations by determining crystal structures of wild-type and NFV-resistant HIV-1 protease in complex with p1-p6 substrate peptide variants with L449F and/or S451N. Alterations of residue 30's interaction with the substrate are compensated by the coevolving L449F and S451N cleavage site mutations. This interdependency in the PR-p1-p6 interactions enhances intermolecular contacts and reinforces the overall fit of the substrate within the substrate envelope, likely enabling coevolution to sustain substrate recognition and cleavage in the presence of PR resistance mutations. Resistance to human immunodeficiency virus type 1 (HIV-1) protease inhibitors challenges the effectiveness of therapies in treating HIV-1-infected individuals and AIDS patients. Mutations in HIV-1 protease selected under the pressure of protease inhibitors render the inhibitors less potent. Occasionally, Gag sequences also mutate and coevolve with protease, contributing to maintenance of viral fitness and to drug resistance. In this study, we investigated the structural basis of coevolution at the Gag p1-p6 cleavage site with the nelfinavir (NFV) resistance D30N/N88D protease mutations. Our structural analysis reveals the interdependency of protease-substrate interactions and how coevolution may restore substrate recognition and cleavage in the presence of protease drug resistance mutations. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Finite-time stability of neutral-type neural networks with random time-varying delays

NASA Astrophysics Data System (ADS)

Ali, M. Syed; Saravanan, S.; Zhu, Quanxin

2017-11-01

This paper is devoted to the finite-time stability analysis of neutral-type neural networks with random time-varying delays. The randomly time-varying delays are characterised by Bernoulli stochastic variable. This result can be extended to analysis and design for neutral-type neural networks with random time-varying delays. On the basis of this paper, we constructed suitable Lyapunov-Krasovskii functional together and established a set of sufficient linear matrix inequalities approach to guarantee the finite-time stability of the system concerned. By employing the Jensen's inequality, free-weighting matrix method and Wirtinger's double integral inequality, the proposed conditions are derived and two numerical examples are addressed for the effectiveness of the developed techniques.

More randomized and resilient in the topological properties of functional brain networks in patients with major depressive disorder.

PubMed

Li, Huaizhou; Zhou, Haiyan; Yang, Yang; Wang, Haiyuan; Zhong, Ning

2017-10-01

Previous studies have reported the enhanced randomization of functional brain networks in patients with major depressive disorder (MDD). However, little is known about the changes of key nodal attributes for randomization, the resilience of network, and the clinical significance of the alterations. In this study, we collected the resting-state functional MRI data from 19 MDD patients and 19 healthy control (HC) individuals. Graph theory analysis showed that decreases were found in the small-worldness, clustering coefficient, local efficiency, and characteristic path length (i.e., increase of global efficiency) in the network of MDD group compared with HC group, which was consistent with previous findings and suggested the development toward randomization in the brain network in MDD. In addition, the greater resilience under the targeted attacks was also found in the network of patients with MDD. Furthermore, the abnormal nodal properties were found, including clustering coefficients and nodal efficiencies in the left orbital superior frontal gyrus, bilateral insula, left amygdala, right supramarginal gyrus, left putamen, left posterior cingulate cortex, left angular gyrus. Meanwhile, the correlation analysis showed that most of these abnormal areas were associated with the clinical status. The observed increased randomization and resilience in MDD might be related to the abnormal hub nodes in the brain networks, which were attacked by the disease pathology. Our findings provide new evidence to indicate that the weakening of specialized regions and the enhancement of whole brain integrity could be the potential endophenotype of the depressive pathology. Copyright © 2017 Elsevier Ltd. All rights reserved.

Reconfiguration and Search of Social Networks

PubMed Central

Zhang, Lianming; Peng, Aoyuan

2013-01-01

Social networks tend to exhibit some topological characteristics different from regular networks and random networks, such as shorter average path length and higher clustering coefficient, and the node degree of the majority of social networks obeys exponential distribution. Based on the topological characteristics of the real social networks, a new network model which suits to portray the structure of social networks was proposed, and the characteristic parameters of the model were calculated. To find out the relationship between two people in the social network, and using the local information of the social network and the parallel mechanism, a hybrid search strategy based on k-walker random and a high degree was proposed. Simulation results show that the strategy can significantly reduce the average number of search steps, so as to effectively improve the search speed and efficiency. PMID:24574861

Attack Vulnerability of Network Controllability

PubMed Central

2016-01-01

Controllability of complex networks has attracted much attention, and understanding the robustness of network controllability against potential attacks and failures is of practical significance. In this paper, we systematically investigate the attack vulnerability of network controllability for the canonical model networks as well as the real-world networks subject to attacks on nodes and edges. The attack strategies are selected based on degree and betweenness centralities calculated for either the initial network or the current network during the removal, among which random failure is as a comparison. It is found that the node-based strategies are often more harmful to the network controllability than the edge-based ones, and so are the recalculated strategies than their counterparts. The Barabási-Albert scale-free model, which has a highly biased structure, proves to be the most vulnerable of the tested model networks. In contrast, the Erdős-Rényi random model, which lacks structural bias, exhibits much better robustness to both node-based and edge-based attacks. We also survey the control robustness of 25 real-world networks, and the numerical results show that most real networks are control robust to random node failures, which has not been observed in the model networks. And the recalculated betweenness-based strategy is the most efficient way to harm the controllability of real-world networks. Besides, we find that the edge degree is not a good quantity to measure the importance of an edge in terms of network controllability. PMID:27588941

Attack Vulnerability of Network Controllability.

PubMed

Lu, Zhe-Ming; Li, Xin-Feng

2016-01-01

Controllability of complex networks has attracted much attention, and understanding the robustness of network controllability against potential attacks and failures is of practical significance. In this paper, we systematically investigate the attack vulnerability of network controllability for the canonical model networks as well as the real-world networks subject to attacks on nodes and edges. The attack strategies are selected based on degree and betweenness centralities calculated for either the initial network or the current network during the removal, among which random failure is as a comparison. It is found that the node-based strategies are often more harmful to the network controllability than the edge-based ones, and so are the recalculated strategies than their counterparts. The Barabási-Albert scale-free model, which has a highly biased structure, proves to be the most vulnerable of the tested model networks. In contrast, the Erdős-Rényi random model, which lacks structural bias, exhibits much better robustness to both node-based and edge-based attacks. We also survey the control robustness of 25 real-world networks, and the numerical results show that most real networks are control robust to random node failures, which has not been observed in the model networks. And the recalculated betweenness-based strategy is the most efficient way to harm the controllability of real-world networks. Besides, we find that the edge degree is not a good quantity to measure the importance of an edge in terms of network controllability.

Unanticipated Effect of a Randomized Peer Network Intervention on Depressive Symptoms among Young Methamphetamine Users in Thailand

ERIC Educational Resources Information Center

German, D.; Sutcliffe, C. G.; Sirirojn, B.; Sherman, S. G.; Latkin, C. A.; Aramrattana, A.; Celentano, D. D.

2012-01-01

We examined the effect on depressive symptoms of a peer network-oriented intervention effective in reducing sexual risk behavior and methamphetamine (MA) use. Current Thai MA users aged 18-25 years and their drug and/or sex network members enrolled in a randomized controlled trial with 4 follow-ups over 12 months. A total of 415 index participants…

Theoretical Foundations of Wireless Networks

DTIC Science & Technology

2015-07-22

Optimal transmission over a fading channel with imperfect channel state information,” in Global Telecommun. Conf., pp. 1–5, Houston TX , December 5-9...SECURITY CLASSIFICATION OF: The goal of this project is to develop a formal theory of wireless networks providing a scientific basis to understand...randomness and optimality. Randomness, in the form of fading, is a defining characteristic of wireless networks. Optimality is a suitable design

From Networks to Time Series

NASA Astrophysics Data System (ADS)

Shimada, Yutaka; Ikeguchi, Tohru; Shigehara, Takaomi

2012-10-01

In this Letter, we propose a framework to transform a complex network to a time series. The transformation from complex networks to time series is realized by the classical multidimensional scaling. Applying the transformation method to a model proposed by Watts and Strogatz [Nature (London) 393, 440 (1998)], we show that ring lattices are transformed to periodic time series, small-world networks to noisy periodic time series, and random networks to random time series. We also show that these relationships are analytically held by using the circulant-matrix theory and the perturbation theory of linear operators. The results are generalized to several high-dimensional lattices.

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.

CONSISTENCY UNDER SAMPLING OF EXPONENTIAL RANDOM GRAPH MODELS.

PubMed

Shalizi, Cosma Rohilla; Rinaldo, Alessandro

2013-04-01

The growing availability of network data and of scientific interest in distributed systems has led to the rapid development of statistical models of network structure. Typically, however, these are models for the entire network, while the data consists only of a sampled sub-network. Parameters for the whole network, which is what is of interest, are estimated by applying the model to the sub-network. This assumes that the model is consistent under sampling , or, in terms of the theory of stochastic processes, that it defines a projective family. Focusing on the popular class of exponential random graph models (ERGMs), we show that this apparently trivial condition is in fact violated by many popular and scientifically appealing models, and that satisfying it drastically limits ERGM's expressive power. These results are actually special cases of more general results about exponential families of dependent random variables, which we also prove. Using such results, we offer easily checked conditions for the consistency of maximum likelihood estimation in ERGMs, and discuss some possible constructive responses.

CONSISTENCY UNDER SAMPLING OF EXPONENTIAL RANDOM GRAPH MODELS

PubMed Central

Shalizi, Cosma Rohilla; Rinaldo, Alessandro

2015-01-01

The growing availability of network data and of scientific interest in distributed systems has led to the rapid development of statistical models of network structure. Typically, however, these are models for the entire network, while the data consists only of a sampled sub-network. Parameters for the whole network, which is what is of interest, are estimated by applying the model to the sub-network. This assumes that the model is consistent under sampling, or, in terms of the theory of stochastic processes, that it defines a projective family. Focusing on the popular class of exponential random graph models (ERGMs), we show that this apparently trivial condition is in fact violated by many popular and scientifically appealing models, and that satisfying it drastically limits ERGM’s expressive power. These results are actually special cases of more general results about exponential families of dependent random variables, which we also prove. Using such results, we offer easily checked conditions for the consistency of maximum likelihood estimation in ERGMs, and discuss some possible constructive responses. PMID:26166910

Network sampling coverage II: The effect of non-random missing data on network measurement

PubMed Central

Smith, Jeffrey A.; Moody, James; Morgan, Jonathan

2016-01-01

Missing data is an important, but often ignored, aspect of a network study. Measurement validity is affected by missing data, but the level of bias can be difficult to gauge. Here, we describe the effect of missing data on network measurement across widely different circumstances. In Part I of this study (Smith and Moody, 2013), we explored the effect of measurement bias due to randomly missing nodes. Here, we drop the assumption that data are missing at random: what happens to estimates of key network statistics when central nodes are more/less likely to be missing? We answer this question using a wide range of empirical networks and network measures. We find that bias is worse when more central nodes are missing. With respect to network measures, Bonacich centrality is highly sensitive to the loss of central nodes, while closeness centrality is not; distance and bicomponent size are more affected than triad summary measures and behavioral homophily is more robust than degree-homophily. With respect to types of networks, larger, directed networks tend to be more robust, but the relation is weak. We end the paper with a practical application, showing how researchers can use our results (translated into a publically available java application) to gauge the bias in their own data. PMID:27867254

Network sampling coverage II: The effect of non-random missing data on network measurement.

PubMed

Smith, Jeffrey A; Moody, James; Morgan, Jonathan

2017-01-01

Missing data is an important, but often ignored, aspect of a network study. Measurement validity is affected by missing data, but the level of bias can be difficult to gauge. Here, we describe the effect of missing data on network measurement across widely different circumstances. In Part I of this study (Smith and Moody, 2013), we explored the effect of measurement bias due to randomly missing nodes. Here, we drop the assumption that data are missing at random: what happens to estimates of key network statistics when central nodes are more/less likely to be missing? We answer this question using a wide range of empirical networks and network measures. We find that bias is worse when more central nodes are missing. With respect to network measures, Bonacich centrality is highly sensitive to the loss of central nodes, while closeness centrality is not; distance and bicomponent size are more affected than triad summary measures and behavioral homophily is more robust than degree-homophily. With respect to types of networks, larger, directed networks tend to be more robust, but the relation is weak. We end the paper with a practical application, showing how researchers can use our results (translated into a publically available java application) to gauge the bias in their own data.

Programmable disorder in random DNA tilings

NASA Astrophysics Data System (ADS)

Tikhomirov, Grigory; Petersen, Philip; Qian, Lulu

2017-03-01

Scaling up the complexity and diversity of synthetic molecular structures will require strategies that exploit the inherent stochasticity of molecular systems in a controlled fashion. Here we demonstrate a framework for programming random DNA tilings and show how to control the properties of global patterns through simple, local rules. We constructed three general forms of planar network—random loops, mazes and trees—on the surface of self-assembled DNA origami arrays on the micrometre scale with nanometre resolution. Using simple molecular building blocks and robust experimental conditions, we demonstrate control of a wide range of properties of the random networks, including the branching rules, the growth directions, the proximity between adjacent networks and the size distribution. Much as combinatorial approaches for generating random one-dimensional chains of polymers have been used to revolutionize chemical synthesis and the selection of functional nucleic acids, our strategy extends these principles to random two-dimensional networks of molecules and creates new opportunities for fabricating more complex molecular devices that are organized by DNA nanostructures.

T-cell movement on the reticular network.

PubMed

Donovan, Graham M; Lythe, Grant

2012-02-21

The idea that the apparently random motion of T cells in lymph nodes is a result of movement on a reticular network (RN) has received support from dynamic imaging experiments and theoretical studies. We present a mathematical representation of the RN consisting of edges connecting vertices that are randomly distributed in three-dimensional space, and models of lymphocyte movement on such networks including constant speed motion along edges and Brownian motion, not in three-dimensions, but only along edges. The simplest model, in which a cell moves with a constant speed along edges, is consistent with mean-squared displacement proportional to time over intervals long enough to include several changes of direction. A non-random distribution of turning angles is one consequence of motion on a preformed network. Confining cell movement to a network does not, in itself, increase the frequency of cell-cell encounters. Copyright © 2011 Elsevier Ltd. All rights reserved.

Dynamical influence processes on networks: general theory and applications to social contagion.

PubMed

Harris, Kameron Decker; Danforth, Christopher M; Dodds, Peter Sheridan

2013-08-01

We study binary state dynamics on a network where each node acts in response to the average state of its neighborhood. By allowing varying amounts of stochasticity in both the network and node responses, we find different outcomes in random and deterministic versions of the model. In the limit of a large, dense network, however, we show that these dynamics coincide. We construct a general mean-field theory for random networks and show this predicts that the dynamics on the network is a smoothed version of the average response function dynamics. Thus, the behavior of the system can range from steady state to chaotic depending on the response functions, network connectivity, and update synchronicity. As a specific example, we model the competing tendencies of imitation and nonconformity by incorporating an off-threshold into standard threshold models of social contagion. In this way, we attempt to capture important aspects of fashions and societal trends. We compare our theory to extensive simulations of this "limited imitation contagion" model on Poisson random graphs, finding agreement between the mean-field theory and stochastic simulations.

Research on Some Bus Transport Networks with Random Overlapping Clique Structure

NASA Astrophysics Data System (ADS)

Yang, Xu-Hua; Wang, Bo; Wang, Wan-Liang; Sun, You-Xian

2008-11-01

On the basis of investigating the statistical data of bus transport networks of three big cities in China, we propose that each bus route is a clique (maximal complete subgraph) and a bus transport network (BTN) consists of a lot of cliques, which intensively connect and overlap with each other. We study the network properties, which include the degree distribution, multiple edges' overlapping time distribution, distribution of the overlap size between any two overlapping cliques, distribution of the number of cliques that a node belongs to. Naturally, the cliques also constitute a network, with the overlapping nodes being their multiple links. We also research its network properties such as degree distribution, clustering, average path length, and so on. We propose that a BTN has the properties of random clique increment and random overlapping clique, at the same time, a BTN is a small-world network with highly clique-clustered and highly clique-overlapped. Finally, we introduce a BTN evolution model, whose simulation results agree well with the statistical laws that emerge in real BTNs.

Finite plateau in spectral gap of polychromatic constrained random networks

NASA Astrophysics Data System (ADS)

Avetisov, V.; Gorsky, A.; Nechaev, S.; Valba, O.

2017-12-01

We consider critical behavior in the ensemble of polychromatic Erdős-Rényi networks and regular random graphs, where network vertices are painted in different colors. The links can be randomly removed and added to the network subject to the condition of the vertex degree conservation. In these constrained graphs we run the Metropolis procedure, which favors the connected unicolor triads of nodes. Changing the chemical potential, μ , of such triads, for some wide region of μ , we find the formation of a finite plateau in the number of intercolor links, which exactly matches the finite plateau in the network algebraic connectivity (the value of the first nonvanishing eigenvalue of the Laplacian matrix, λ2). We claim that at the plateau the spontaneously broken Z2 symmetry is restored by the mechanism of modes collectivization in clusters of different colors. The phenomena of a finite plateau formation holds also for polychromatic networks with M ≥2 colors. The behavior of polychromatic networks is analyzed via the spectral properties of their adjacency and Laplacian matrices.

Random network peristalsis in Physarum polycephalum organizes fluid flows across an individual

PubMed Central

Alim, Karen; Amselem, Gabriel; Peaudecerf, François; Brenner, Michael P.; Pringle, Anne

2013-01-01

Individuals can function as integrated organisms only when information and resources are shared across a body. Signals and substrates are commonly moved using fluids, often channeled through a network of tubes. Peristalsis is one mechanism for fluid transport and is caused by a wave of cross-sectional contractions along a tube. We extend the concept of peristalsis from the canonical case of one tube to a random network. Transport is maximized within the network when the wavelength of the peristaltic wave is of the order of the size of the network. The slime mold Physarum polycephalum grows as a random network of tubes, and our experiments confirm peristalsis is used by the slime mold to drive internal cytoplasmic flows. Comparisons of theoretically generated contraction patterns with the patterns exhibited by individuals of P. polycephalum demonstrate that individuals maximize internal flows by adapting patterns of contraction to size, thus optimizing transport throughout an organism. This control of fluid flow may be the key to coordinating growth and behavior, including the dynamic changes in network architecture seen over time in an individual. PMID:23898203

Random network peristalsis in Physarum polycephalum organizes fluid flows across an individual.

PubMed

Alim, Karen; Amselem, Gabriel; Peaudecerf, François; Brenner, Michael P; Pringle, Anne

2013-08-13

Individuals can function as integrated organisms only when information and resources are shared across a body. Signals and substrates are commonly moved using fluids, often channeled through a network of tubes. Peristalsis is one mechanism for fluid transport and is caused by a wave of cross-sectional contractions along a tube. We extend the concept of peristalsis from the canonical case of one tube to a random network. Transport is maximized within the network when the wavelength of the peristaltic wave is of the order of the size of the network. The slime mold Physarum polycephalum grows as a random network of tubes, and our experiments confirm peristalsis is used by the slime mold to drive internal cytoplasmic flows. Comparisons of theoretically generated contraction patterns with the patterns exhibited by individuals of P. polycephalum demonstrate that individuals maximize internal flows by adapting patterns of contraction to size, thus optimizing transport throughout an organism. This control of fluid flow may be the key to coordinating growth and behavior, including the dynamic changes in network architecture seen over time in an individual.

Projective-anticipating, projective, and projective-lag synchronization of time-delayed chaotic systems on random networks.

PubMed

Feng, Cun-Fang; Xu, Xin-Jian; Wang, Sheng-Jun; Wang, Ying-Hai

2008-06-01

We study projective-anticipating, projective, and projective-lag synchronization of time-delayed chaotic systems on random networks. We relax some limitations of previous work, where projective-anticipating and projective-lag synchronization can be achieved only on two coupled chaotic systems. In this paper, we realize projective-anticipating and projective-lag synchronization on complex dynamical networks composed of a large number of interconnected components. At the same time, although previous work studied projective synchronization on complex dynamical networks, the dynamics of the nodes are coupled partially linear chaotic systems. In this paper, the dynamics of the nodes of the complex networks are time-delayed chaotic systems without the limitation of the partial linearity. Based on the Lyapunov stability theory, we suggest a generic method to achieve the projective-anticipating, projective, and projective-lag synchronization of time-delayed chaotic systems on random dynamical networks, and we find both its existence and sufficient stability conditions. The validity of the proposed method is demonstrated and verified by examining specific examples using Ikeda and Mackey-Glass systems on Erdos-Renyi networks.

Random matrix theory for analyzing the brain functional network in attention deficit hyperactivity disorder

NASA Astrophysics Data System (ADS)

Wang, Rong; Wang, Li; Yang, Yong; Li, Jiajia; Wu, Ying; Lin, Pan

2016-11-01

Attention deficit hyperactivity disorder (ADHD) is the most common childhood neuropsychiatric disorder and affects approximately 6 -7 % of children worldwide. Here, we investigate the statistical properties of undirected and directed brain functional networks in ADHD patients based on random matrix theory (RMT), in which the undirected functional connectivity is constructed based on correlation coefficient and the directed functional connectivity is measured based on cross-correlation coefficient and mutual information. We first analyze the functional connectivity and the eigenvalues of the brain functional network. We find that ADHD patients have increased undirected functional connectivity, reflecting a higher degree of linear dependence between regions, and increased directed functional connectivity, indicating stronger causality and more transmission of information among brain regions. More importantly, we explore the randomness of the undirected and directed functional networks using RMT. We find that for ADHD patients, the undirected functional network is more orderly than that for normal subjects, which indicates an abnormal increase in undirected functional connectivity. In addition, we find that the directed functional networks are more random, which reveals greater disorder in causality and more chaotic information flow among brain regions in ADHD patients. Our results not only further confirm the efficacy of RMT in characterizing the intrinsic properties of brain functional networks but also provide insights into the possibilities RMT offers for improving clinical diagnoses and treatment evaluations for ADHD patients.

Distributed clone detection in static wireless sensor networks: random walk with network division.

PubMed

Khan, Wazir Zada; Aalsalem, Mohammed Y; Saad, N M

2015-01-01

Wireless Sensor Networks (WSNs) are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND) for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s) by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.

Social power and opinion formation in complex networks

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2013-02-01

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

Random vs. Combinatorial Methods for Discrete Event Simulation of a Grid Computer Network

NASA Technical Reports Server (NTRS)

Kuhn, D. Richard; Kacker, Raghu; Lei, Yu

2010-01-01

This study compared random and t-way combinatorial inputs of a network simulator, to determine if these two approaches produce significantly different deadlock detection for varying network configurations. Modeling deadlock detection is important for analyzing configuration changes that could inadvertently degrade network operations, or to determine modifications that could be made by attackers to deliberately induce deadlock. Discrete event simulation of a network may be conducted using random generation, of inputs. In this study, we compare random with combinatorial generation of inputs. Combinatorial (or t-way) testing requires every combination of any t parameter values to be covered by at least one test. Combinatorial methods can be highly effective because empirical data suggest that nearly all failures involve the interaction of a small number of parameters (1 to 6). Thus, for example, if all deadlocks involve at most 5-way interactions between n parameters, then exhaustive testing of all n-way interactions adds no additional information that would not be obtained by testing all 5-way interactions. While the maximum degree of interaction between parameters involved in the deadlocks clearly cannot be known in advance, covering all t-way interactions may be more efficient than using random generation of inputs. In this study we tested this hypothesis for t = 2, 3, and 4 for deadlock detection in a network simulation. Achieving the same degree of coverage provided by 4-way tests would have required approximately 3.2 times as many random tests; thus combinatorial methods were more efficient for detecting deadlocks involving a higher degree of interactions. The paper reviews explanations for these results and implications for modeling and simulation.

Spectra of random networks in the weak clustering regime

NASA Astrophysics Data System (ADS)

Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen; Rodrigues, Francisco A.

2018-03-01

The asymptotic behavior of dynamical processes in networks can be expressed as a function of spectral properties of the corresponding adjacency and Laplacian matrices. Although many theoretical results are known for the spectra of traditional configuration models, networks generated through these models fail to describe many topological features of real-world networks, in particular non-null values of the clustering coefficient. Here we study effects of cycles of order three (triangles) in network spectra. By using recent advances in random matrix theory, we determine the spectral distribution of the network adjacency matrix as a function of the average number of triangles attached to each node for networks without modular structure and degree-degree correlations. Implications to network dynamics are discussed. Our findings can shed light in the study of how particular kinds of subgraphs influence network dynamics.

Spectral statistics of random geometric graphs

NASA Astrophysics Data System (ADS)

Dettmann, C. P.; Georgiou, O.; Knight, G.

2017-04-01

We use random matrix theory to study the spectrum of random geometric graphs, a fundamental model of spatial networks. Considering ensembles of random geometric graphs we look at short-range correlations in the level spacings of the spectrum via the nearest-neighbour and next-nearest-neighbour spacing distribution and long-range correlations via the spectral rigidity Δ3 statistic. These correlations in the level spacings give information about localisation of eigenvectors, level of community structure and the level of randomness within the networks. We find a parameter-dependent transition between Poisson and Gaussian orthogonal ensemble statistics. That is the spectral statistics of spatial random geometric graphs fits the universality of random matrix theory found in other models such as Erdős-Rényi, Barabási-Albert and Watts-Strogatz random graphs.

Resistance and Security Index of Networks: Structural Information Perspective of Network Security

NASA Astrophysics Data System (ADS)

Li, Angsheng; Hu, Qifu; Liu, Jun; Pan, Yicheng

2016-06-01

Recently, Li and Pan defined the metric of the K-dimensional structure entropy of a structured noisy dataset G to be the information that controls the formation of the K-dimensional structure of G that is evolved by the rules, order and laws of G, excluding the random variations that occur in G. Here, we propose the notion of resistance of networks based on the one- and two-dimensional structural information of graphs. Given a graph G, we define the resistance of G, written , as the greatest overall number of bits required to determine the code of the module that is accessible via random walks with stationary distribution in G, from which the random walks cannot escape. We show that the resistance of networks follows the resistance law of networks, that is, for a network G, the resistance of G is , where and are the one- and two-dimensional structure entropies of G, respectively. Based on the resistance law, we define the security index of a network G to be the normalised resistance of G, that is, . We show that the resistance and security index are both well-defined measures for the security of the networks.

Randomizing growing networks with a time-respecting null model

NASA Astrophysics Data System (ADS)

Ren, Zhuo-Ming; Mariani, Manuel Sebastian; Zhang, Yi-Cheng; Medo, Matúš

2018-05-01

Complex networks are often used to represent systems that are not static but grow with time: People make new friendships, new papers are published and refer to the existing ones, and so forth. To assess the statistical significance of measurements made on such networks, we propose a randomization methodology—a time-respecting null model—that preserves both the network's degree sequence and the time evolution of individual nodes' degree values. By preserving the temporal linking patterns of the analyzed system, the proposed model is able to factor out the effect of the system's temporal patterns on its structure. We apply the model to the citation network of Physical Review scholarly papers and the citation network of US movies. The model reveals that the two data sets are strikingly different with respect to their degree-degree correlations, and we discuss the important implications of this finding on the information provided by paradigmatic node centrality metrics such as indegree and Google's PageRank. The randomization methodology proposed here can be used to assess the significance of any structural property in growing networks, which could bring new insights into the problems where null models play a critical role, such as the detection of communities and network motifs.

Resistance and Security Index of Networks: Structural Information Perspective of Network Security.

PubMed

Li, Angsheng; Hu, Qifu; Liu, Jun; Pan, Yicheng

2016-06-03

Recently, Li and Pan defined the metric of the K-dimensional structure entropy of a structured noisy dataset G to be the information that controls the formation of the K-dimensional structure of G that is evolved by the rules, order and laws of G, excluding the random variations that occur in G. Here, we propose the notion of resistance of networks based on the one- and two-dimensional structural information of graphs. Given a graph G, we define the resistance of G, written , as the greatest overall number of bits required to determine the code of the module that is accessible via random walks with stationary distribution in G, from which the random walks cannot escape. We show that the resistance of networks follows the resistance law of networks, that is, for a network G, the resistance of G is , where and are the one- and two-dimensional structure entropies of G, respectively. Based on the resistance law, we define the security index of a network G to be the normalised resistance of G, that is, . We show that the resistance and security index are both well-defined measures for the security of the networks.

Resistance and Security Index of Networks: Structural Information Perspective of Network Security

PubMed Central

Li, Angsheng; Hu, Qifu; Liu, Jun; Pan, Yicheng

2016-01-01

Recently, Li and Pan defined the metric of the K-dimensional structure entropy of a structured noisy dataset G to be the information that controls the formation of the K-dimensional structure of G that is evolved by the rules, order and laws of G, excluding the random variations that occur in G. Here, we propose the notion of resistance of networks based on the one- and two-dimensional structural information of graphs. Given a graph G, we define the resistance of G, written , as the greatest overall number of bits required to determine the code of the module that is accessible via random walks with stationary distribution in G, from which the random walks cannot escape. We show that the resistance of networks follows the resistance law of networks, that is, for a network G, the resistance of G is , where and are the one- and two-dimensional structure entropies of G, respectively. Based on the resistance law, we define the security index of a network G to be the normalised resistance of G, that is, . We show that the resistance and security index are both well-defined measures for the security of the networks. PMID:27255783

Controllability of social networks and the strategic use of random information.

PubMed

Cremonini, Marco; Casamassima, Francesca

2017-01-01

This work is aimed at studying realistic social control strategies for social networks based on the introduction of random information into the state of selected driver agents. Deliberately exposing selected agents to random information is a technique already experimented in recommender systems or search engines, and represents one of the few options for influencing the behavior of a social context that could be accepted as ethical, could be fully disclosed to members, and does not involve the use of force or of deception. Our research is based on a model of knowledge diffusion applied to a time-varying adaptive network and considers two well-known strategies for influencing social contexts: One is the selection of few influencers for manipulating their actions in order to drive the whole network to a certain behavior; the other, instead, drives the network behavior acting on the state of a large subset of ordinary, scarcely influencing users. The two approaches have been studied in terms of network and diffusion effects. The network effect is analyzed through the changes induced on network average degree and clustering coefficient, while the diffusion effect is based on two ad hoc metrics which are defined to measure the degree of knowledge diffusion and skill level, as well as the polarization of agent interests. The results, obtained through simulations on synthetic networks, show a rich dynamics and strong effects on the communication structure and on the distribution of knowledge and skills. These findings support our hypothesis that the strategic use of random information could represent a realistic approach to social network controllability, and that with both strategies, in principle, the control effect could be remarkable.

Random Resistor Network Model of Minimal Conductivity in Graphene

NASA Astrophysics Data System (ADS)

Cheianov, Vadim V.; Fal'Ko, Vladimir I.; Altshuler, Boris L.; Aleiner, Igor L.

2007-10-01

Transport in undoped graphene is related to percolating current patterns in the networks of n- and p-type regions reflecting the strong bipolar charge density fluctuations. Finite transparency of the p-n junctions is vital in establishing the macroscopic conductivity. We propose a random resistor network model to analyze scaling dependencies of the conductance on the doping and disorder, the quantum magnetoresistance and the corresponding dephasing rate.

Enhanced Precision Geolocation in 4G Wireless Networks

DTIC Science & Technology

2013-03-01

years has implemented a National Emergency Warning System using text messages delivered to cell phones [5]. The November 1999 FCC E911 regulations...statistical theory of passive geolocation of emitters may be found in [18]. Papers that survey methods of geolocation applied to cell phones include [4...where to put the tower % n: which tower to place %randomTowers(obj,dispersion, seperation ): generates % random towers for the network % obj: the network

Mapping Flexibility and the Assembly Switch of Cell Division Protein FtsZ by Computational and Mutational Approaches*♦

PubMed Central

Martín-Galiano, Antonio J.; Buey, Rubén M.; Cabezas, Marta; Andreu, José M.

2010-01-01

The molecular switch for nucleotide-regulated assembly and disassembly of the main prokaryotic cell division protein FtsZ is unknown despite the numerous crystal structures that are available. We have characterized the functional motions in FtsZ with a computational consensus of essential dynamics, structural comparisons, sequence conservation, and networks of co-evolving residues. Employing this information, we have constructed 17 mutants, which alter the FtsZ functional cycle at different stages, to modify FtsZ flexibility. The mutant phenotypes ranged from benign to total inactivation and included increased GTPase, reduced assembly, and stabilized assembly. Six mutations clustering at the long cleft between the C-terminal β-sheet and core helix H7 deviated FtsZ assembly into curved filaments with inhibited GTPase, which still polymerize cooperatively. These mutations may perturb the predicted closure of the C-terminal domain onto H7 required for switching between curved and straight association modes and for GTPase activation. By mapping the FtsZ assembly switch, this work also gives insight into FtsZ druggability because the curved mutations delineate the putative binding site of the promising antibacterial FtsZ inhibitor PC190723. PMID:20472561

The evolution and function of vessel and pit characters with respect to cavitation resistance across 10 Prunus species.

PubMed

Scholz, Alexander; Rabaey, David; Stein, Anke; Cochard, Hervé; Smets, Erik; Jansen, Steven

2013-07-01

Various structure-function relationships regarding drought-induced cavitation resistance of secondary xylem have been postulated. These hypotheses were tested on wood of 10 Prunus species showing a range in P50 (i.e., the pressure corresponding to 50% loss of hydraulic conductivity) from -3.54 to -6.27 MPa. Hydraulically relevant wood characters were quantified using light and electron microscopy. A phylogenetic tree was constructed to investigate evolutionary correlations using a phylogenetically independent contrast (PIC) analysis. Vessel-grouping characters were found to be most informative in explaining interspecific variation in P50, with cavitation-resistant species showing more solitary vessels than less resistant species. Co-evolution between vessel-grouping indices and P50 was reported. P50 was weakly correlated with the shape of the intervessel pit aperture, but not with the total intervessel pit membrane area per vessel. A negative correlation was found between P50 and intervessel pit membrane thickness, but this relationship was not supported by the PIC analysis. Cavitation resistance has co-evolved with vessel grouping within Prunus and was mainly influenced by the spatial distribution of the vessel network.

Mapping flexibility and the assembly switch of cell division protein FtsZ by computational and mutational approaches.

PubMed

Martín-Galiano, Antonio J; Buey, Rubén M; Cabezas, Marta; Andreu, José M

2010-07-16

The molecular switch for nucleotide-regulated assembly and disassembly of the main prokaryotic cell division protein FtsZ is unknown despite the numerous crystal structures that are available. We have characterized the functional motions in FtsZ with a computational consensus of essential dynamics, structural comparisons, sequence conservation, and networks of co-evolving residues. Employing this information, we have constructed 17 mutants, which alter the FtsZ functional cycle at different stages, to modify FtsZ flexibility. The mutant phenotypes ranged from benign to total inactivation and included increased GTPase, reduced assembly, and stabilized assembly. Six mutations clustering at the long cleft between the C-terminal beta-sheet and core helix H7 deviated FtsZ assembly into curved filaments with inhibited GTPase, which still polymerize cooperatively. These mutations may perturb the predicted closure of the C-terminal domain onto H7 required for switching between curved and straight association modes and for GTPase activation. By mapping the FtsZ assembly switch, this work also gives insight into FtsZ druggability because the curved mutations delineate the putative binding site of the promising antibacterial FtsZ inhibitor PC190723.

The spatial architecture of protein function and adaptation

PubMed Central

McLaughlin, Richard N.; Poelwijk, Frank J.; Raman, Arjun; Gosal, Walraj S.; Ranganathan, Rama

2014-01-01

Statistical analysis of protein evolution suggests a design for natural proteins in which sparse networks of coevolving amino acids (termed sectors) comprise the essence of three-dimensional structure and function1, 2, 3, 4, 5. However, proteins are also subject to pressures deriving from the dynamics of the evolutionary process itself—the ability to tolerate mutation and to be adaptive to changing selection pressures6, 7, 8, 9, 10. To understand the relationship of the sector architecture to these properties, we developed a high-throughput quantitative method for a comprehensive single-mutation study in which every position is substituted individually to every other amino acid. Using a PDZ domain (PSD95pdz3) model system, we show that sector positions are functionally sensitive to mutation, whereas non-sector positions are more tolerant to substitution. In addition, we find that adaptation to a new binding specificity initiates exclusively through variation within sector residues. A combination of just two sector mutations located near and away from the ligand-binding site suffices to switch the binding specificity of PSD95pdz3 quantitatively towards a class-switching ligand. The localization of functional constraint and adaptive variation within the sector has important implications for understanding and engineering proteins. PMID:23041932

TEACH: An Ethogram-Based Method to Observe and Record Teaching Behavior

ERIC Educational Resources Information Center

Kline, Michelle Ann

2017-01-01

Teaching has attracted growing research attention in studies of human and animal behavior as a crucial behavior that coevolved with human cultural capacities. However, the synthesis of data on teaching across species and across human populations has proven elusive because researchers use a variety of definitions and methods to approach the topic.…

The Lecture as a Transmedial Pedagogical Form: A Historical Analysis

ERIC Educational Resources Information Center

Friesen, Norm

2011-01-01

The lecture has been much maligned as a pedagogical form, yet it persists and even flourishes today in the form of the podcast, the TED talk, and the "smart" lecture hall. This article examines the lecture as a pedagogical genre, as "a site where differences between media are negotiated" (Franzel) as these media coevolve. This examination shows…

Big Role for a Tiny Genome.

PubMed

Douglas, Angela E

2017-12-14

In this issue of Cell, Salem et al. demonstrate a remarkable instance of herbivory dependent on a co-evolved mutualism with specialized bacteria. Despite having a tiny genome and limited metabolic repertoire, the bacteria in Cassida beetles produce pectinases predicted to mediate degradation of plant cell walls in the insect diet. Copyright © 2017 Elsevier Inc. All rights reserved.

Population genetics and biological control of goldspotted oak borer, an invasive pest of California oaks

Treesearch

Vanessa Lopez; Paul F. Rugman-Jones; Tom W. Coleman; Richard Stouthamer; Mark Hoddle

2015-01-01

California’s oak woodlands are threatened by the recent introduction of goldspotted oak borer (Agrilus auroguttatus). This invasive wood-borer is indigenous to mountain ranges in southern Arizona where its low population densities may be due to the presence of co-evolved, host-specific natural enemies. Reuniting A. auroguttatus...

The political ecology of forest health in the redwood region

Treesearch

Chris Lee; Yana Valachovic; Dan Stark

2017-01-01

Imported forest pests have changed North American forests and caused staggering monetary losses in the centuries since the country was founded. Since most problem-causing non-native pests are innocuous in their home ranges, where they have coevolved with their host trees, experts cannot predict which pathogens or insects will have lethal effect on other continents....

Root disease and exotic ecosystems: implications for long-term site productivity

Treesearch

W.J. Otrosina; M. Garbelotto

1998-01-01

Root disease fungi, particularly root-rotting Basidiomycetes, are key drivers of forest ecosystems. These fungi have co?evolved with their hosts in various forest ecosystems and are in various states of equilibrium with them. Management activities and various land uses have taken place in recent times that have dramatically altered edaphic and environmental conditions...

High-throughput genotyping-by-sequencing facilitates molecular tagging of a novel rust resistance gene, R15, in sunflower (Helianthus annuus L.)

USDA-ARS?s Scientific Manuscript database

The rust virulence gene is co-evolving with the resistance gene in sunflower, leading to the emergence of new physiologic pathotypes. This presents a continuous threat to the sunflower crop necessitating the development of resistant sunflower hybrids providing a more efficient, durable, and environm...

Environmental Stress and Human Migration in a Low-lying Developing Nation: A Comparison of Co-evolving Natural and Human Landscapes in the Physically and Culturally Diverse Context of Bangladesh

DTIC Science & Technology

2012-09-30

different livelihood strategies? How have community resilience and household vulnerability changed in response to engineered structures, shifting...impacts of environmental change by strengthening the factors that contribute to community resilience . 6 IMPACT/APPLICATIONS Our research will

Method and apparatus for routing data in an inter-nodal communications lattice of a massively parallel computer system by semi-randomly varying routing policies for different packets

DOEpatents

Archer, Charles Jens; Musselman, Roy Glenn; Peters, Amanda; Pinnow, Kurt Walter; Swartz, Brent Allen; Wallenfelt, Brian Paul

2010-11-23

A massively parallel computer system contains an inter-nodal communications network of node-to-node links. Nodes vary a choice of routing policy for routing data in the network in a semi-random manner, so that similarly situated packets are not always routed along the same path. Semi-random variation of the routing policy tends to avoid certain local hot spots of network activity, which might otherwise arise using more consistent routing determinations. Preferably, the originating node chooses a routing policy for a packet, and all intermediate nodes in the path route the packet according to that policy. Policies may be rotated on a round-robin basis, selected by generating a random number, or otherwise varied.

Oscillations and chaos in neural networks: an exactly solvable model.

PubMed Central

Wang, L P; Pichler, E E; Ross, J

1990-01-01

We consider a randomly diluted higher-order network with noise, consisting of McCulloch-Pitts neurons that interact by Hebbian-type connections. For this model, exact dynamical equations are derived and solved for both parallel and random sequential updating algorithms. For parallel dynamics, we find a rich spectrum of different behaviors including static retrieving and oscillatory and chaotic phenomena in different parts of the parameter space. The bifurcation parameters include first- and second-order neuronal interaction coefficients and a rescaled noise level, which represents the combined effects of the random synaptic dilution, interference between stored patterns, and additional background noise. We show that a marked difference in terms of the occurrence of oscillations or chaos exists between neural networks with parallel and random sequential dynamics. Images PMID:2251287

Social patterns revealed through random matrix theory

NASA Astrophysics Data System (ADS)

Sarkar, Camellia; Jalan, Sarika

2014-11-01

Despite the tremendous advancements in the field of network theory, very few studies have taken weights in the interactions into consideration that emerge naturally in all real-world systems. Using random matrix analysis of a weighted social network, we demonstrate the profound impact of weights in interactions on emerging structural properties. The analysis reveals that randomness existing in particular time frame affects the decisions of individuals rendering them more freedom of choice in situations of financial security. While the structural organization of networks remains the same throughout all datasets, random matrix theory provides insight into the interaction pattern of individuals of the society in situations of crisis. It has also been contemplated that individual accountability in terms of weighted interactions remains as a key to success unless segregation of tasks comes into play.

Analytic method for calculating properties of random walks on networks

NASA Technical Reports Server (NTRS)

Goldhirsch, I.; Gefen, Y.

1986-01-01

A method for calculating the properties of discrete random walks on networks is presented. The method divides complex networks into simpler units whose contribution to the mean first-passage time is calculated. The simplified network is then further iterated. The method is demonstrated by calculating mean first-passage times on a segment, a segment with a single dangling bond, a segment with many dangling bonds, and a looplike structure. The results are analyzed and related to the applicability of the Einstein relation between conductance and diffusion.

Robustness and structure of complex networks

NASA Astrophysics Data System (ADS)

Shao, Shuai

This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack -- localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks. Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack -- localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system. In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component Pinfinity. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erdoḧs-Renyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent lambda. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks are much more vulnerable to localized attack compared with random attack. In the second part, we extend the tree-like generating function method to incorporating clustering structure in complex networks. We study the robustness of a complex network system, especially a network of networks (NON) with clustering structure in each network. We find that the system becomes less robust as we increase the clustering coefficient of each network. For a partially dependent network system, we also find that the influence of the clustering coefficient on network robustness decreases as we decrease the coupling strength, and the critical coupling strength qc, at which the first-order phase transition changes to second-order, increases as we increase the clustering coefficient.

Robustness analysis of interdependent networks under multiple-attacking strategies

NASA Astrophysics Data System (ADS)

Gao, Yan-Li; Chen, Shi-Ming; Nie, Sen; Ma, Fei; Guan, Jun-Jie

2018-04-01

The robustness of complex networks under attacks largely depends on the structure of a network and the nature of the attacks. Previous research on interdependent networks has focused on two types of initial attack: random attack and degree-based targeted attack. In this paper, a deliberate attack function is proposed, where six kinds of deliberate attacking strategies can be derived by adjusting the tunable parameters. Moreover, the robustness of four types of interdependent networks (BA-BA, ER-ER, BA-ER and ER-BA) with different coupling modes (random, positive and negative correlation) is evaluated under different attacking strategies. Interesting conclusions could be obtained. It can be found that the positive coupling mode can make the vulnerability of the interdependent network to be absolutely dependent on the most vulnerable sub-network under deliberate attacks, whereas random and negative coupling modes make the vulnerability of interdependent network to be mainly dependent on the being attacked sub-network. The robustness of interdependent network will be enhanced with the degree-degree correlation coefficient varying from positive to negative. Therefore, The negative coupling mode is relatively more optimal than others, which can substantially improve the robustness of the ER-ER network and ER-BA network. In terms of the attacking strategies on interdependent networks, the degree information of node is more valuable than the betweenness. In addition, we found a more efficient attacking strategy for each coupled interdependent network and proposed the corresponding protection strategy for suppressing cascading failure. Our results can be very useful for safety design and protection of interdependent networks.

Plant-parasite coevolution: bridging the gap between genetics and ecology.

PubMed

Brown, James K M; Tellier, Aurélien

2011-01-01

We review current ideas about coevolution of plants and parasites, particularly processes that generate genetic diversity. Frequencies of host resistance and parasite virulence alleles that interact in gene-for-gene (GFG) relationships coevolve in the familiar boom-and-bust cycle, in which resistance is selected when virulence is rare, and virulence is selected when resistance is common. The cycle can result in stable polymorphism when diverse ecological and epidemiological factors cause negative direct frequency-dependent selection (ndFDS) on host resistance, parasite virulence, or both, such that the benefit of a trait to fitness declines as its frequency increases. Polymorphism can also be stabilized by overdominance, when heterozygous hosts have greater resistance than homozygotes to diverse pathogens. Genetic diversity can also persist in the form of statistical polymorphism, sustained by random processes acting on gene frequencies and population size. Stable polymorphism allows alleles to be long-lived and genetic variation to be detectable in natural populations. In agriculture, many of the factors promoting stability in host-parasite interactions have been lost, leading to arms races of host defenses and parasite effectors. Copyright © 2011 by Annual Reviews. All rights reserved.

Network meta-analysis, electrical networks and graph theory.

PubMed

Rücker, Gerta

2012-12-01

Network meta-analysis is an active field of research in clinical biostatistics. It aims to combine information from all randomized comparisons among a set of treatments for a given medical condition. We show how graph-theoretical methods can be applied to network meta-analysis. A meta-analytic graph consists of vertices (treatments) and edges (randomized comparisons). We illustrate the correspondence between meta-analytic networks and electrical networks, where variance corresponds to resistance, treatment effects to voltage, and weighted treatment effects to current flows. Based thereon, we then show that graph-theoretical methods that have been routinely applied to electrical networks also work well in network meta-analysis. In more detail, the resulting consistent treatment effects induced in the edges can be estimated via the Moore-Penrose pseudoinverse of the Laplacian matrix. Moreover, the variances of the treatment effects are estimated in analogy to electrical effective resistances. It is shown that this method, being computationally simple, leads to the usual fixed effect model estimate when applied to pairwise meta-analysis and is consistent with published results when applied to network meta-analysis examples from the literature. Moreover, problems of heterogeneity and inconsistency, random effects modeling and including multi-armed trials are addressed. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

On the estimation variance for the specific Euler-Poincaré characteristic of random networks.

PubMed

Tscheschel, A; Stoyan, D

2003-07-01

The specific Euler number is an important topological characteristic in many applications. It is considered here for the case of random networks, which may appear in microscopy either as primary objects of investigation or as secondary objects describing in an approximate way other structures such as, for example, porous media. For random networks there is a simple and natural estimator of the specific Euler number. For its estimation variance, a simple Poisson approximation is given. It is based on the general exact formula for the estimation variance. In two examples of quite different nature and topology application of the formulas is demonstrated.

Relaxation dynamics of maximally clustered networks

NASA Astrophysics Data System (ADS)

Klaise, Janis; Johnson, Samuel

2018-01-01

We study the relaxation dynamics of fully clustered networks (maximal number of triangles) to an unclustered state under two different edge dynamics—the double-edge swap, corresponding to degree-preserving randomization of the configuration model, and single edge replacement, corresponding to full randomization of the Erdős-Rényi random graph. We derive expressions for the time evolution of the degree distribution, edge multiplicity distribution and clustering coefficient. We show that under both dynamics networks undergo a continuous phase transition in which a giant connected component is formed. We calculate the position of the phase transition analytically using the Erdős-Rényi phenomenology.

Ryu-Takayanagi formula for symmetric random tensor networks

NASA Astrophysics Data System (ADS)

Chirco, Goffredo; Oriti, Daniele; Zhang, Mingyi

2018-06-01

We consider the special case of random tensor networks (RTNs) endowed with gauge symmetry constraints on each tensor. We compute the Rényi entropy for such states and recover the Ryu-Takayanagi (RT) formula in the large-bond regime. The result provides first of all an interesting new extension of the existing derivations of the RT formula for RTNs. Moreover, this extension of the RTN formalism brings it in direct relation with (tensorial) group field theories (and spin networks), and thus provides new tools for realizing the tensor network/geometry duality in the context of background-independent quantum gravity, and for importing quantum gravity tools into tensor network research.

Adaptive Evolution of Signaling Partners

PubMed Central

Urano, Daisuke; Dong, Taoran; Bennetzen, Jeffrey L.; Jones, Alan M.

2015-01-01

Proteins that interact coevolve their structures. When mutation disrupts the interaction, compensation by the partner occurs to restore interaction otherwise counterselection occurs. We show in this study how a destabilizing mutation in one protein is compensated by a stabilizing mutation in its protein partner and their coevolving path. The pathway in this case and likely a general principle of coevolution is that the compensatory change must tolerate both the original and derived structures with equivalence in function and activity. Evolution of the structure of signaling elements in a network is constrained by specific protein pair interactions, by requisite conformational changes, and by catalytic activity. The heterotrimeric G protein-coupled signaling is a paragon of this protein interaction/function complexity and our deep understanding of this pathway in diverse organisms lends itself to evolutionary study. Regulators of G protein Signaling (RGS) proteins accelerate the intrinsic GTP hydrolysis rate of the Gα subunit of the heterotrimeric G protein complex. An important RGS-contact site is a hydroxyl-bearing residue on the switch I region of Gα subunits in animals and most plants, such as Arabidopsis. The exception is the grasses (e.g., rice, maize, sugarcane, millets); these plants have Gα subunits that replaced the critical hydroxyl-bearing threonine with a destabilizing asparagine shown to disrupt interaction between Arabidopsis RGS protein (AtRGS1) and the grass Gα subunit. With one known exception (Setaria italica), grasses do not encode RGS genes. One parsimonious deduction is that the RGS gene was lost in the ancestor to the grasses and then recently acquired horizontally in the lineage S. italica from a nongrass monocot. Like all investigated grasses, S. italica has the Gα subunit with the destabilizing asparagine residue in the protein interface but, unlike other known grass genomes, still encodes an expressed RGS gene, SiRGS1. SiRGS1 accelerates GTP hydrolysis at similar concentration of both Gα subunits containing either the stabilizing (AtGPA1) or destabilizing (RGA1) interface residue. SiRGS1 does not use the hydroxyl-bearing residue on Gα to promote GAP activity and has a larger Gα-interface pocket fitting to the destabilizing Gα. These findings indicate that SiRGS1 adapted to a deleterious mutation on Gα using existing polymorphism in the RGS protein population. PMID:25568345

Autocatalytic polymerization generates persistent random walk of crawling cells.

PubMed

Sambeth, R; Baumgaertner, A

2001-05-28

The autocatalytic polymerization kinetics of the cytoskeletal actin network provides the basic mechanism for a persistent random walk of a crawling cell. It is shown that network remodeling by branching processes near the cell membrane is essential for the bimodal spatial stability of the network which induces a spontaneous breaking of isotropic cell motion. Details of the phenomena are analyzed using a simple polymerization model studied by analytical and simulation methods.

Recovery of infrastructure networks after localised attacks.

PubMed

Hu, Fuyu; Yeung, Chi Ho; Yang, Saini; Wang, Weiping; Zeng, An

2016-04-14

The stability of infrastructure network is always a critical issue studied by researchers in different fields. A lot of works have been devoted to reveal the robustness of the infrastructure networks against random and malicious attacks. However, real attack scenarios such as earthquakes and typhoons are instead localised attacks which are investigated only recently. Unlike previous studies, we examine in this paper the resilience of infrastructure networks by focusing on the recovery process from localised attacks. We introduce various preferential repair strategies and found that they facilitate and improve network recovery compared to that of random repairs, especially when population size is uneven at different locations. Moreover, our strategic repair methods show similar effectiveness as the greedy repair. The validations are conducted on simulated networks, and on real networks with real disasters. Our method is meaningful in practice as it can largely enhance network resilience and contribute to network risk reduction.

Describing spatial pattern in stream networks: A practical approach

USGS Publications Warehouse

Ganio, L.M.; Torgersen, C.E.; Gresswell, R.E.

2005-01-01

The shape and configuration of branched networks influence ecological patterns and processes. Recent investigations of network influences in riverine ecology stress the need to quantify spatial structure not only in a two-dimensional plane, but also in networks. An initial step in understanding data from stream networks is discerning non-random patterns along the network. On the other hand, data collected in the network may be spatially autocorrelated and thus not suitable for traditional statistical analyses. Here we provide a method that uses commercially available software to construct an empirical variogram to describe spatial pattern in the relative abundance of coastal cutthroat trout in headwater stream networks. We describe the mathematical and practical considerations involved in calculating a variogram using a non-Euclidean distance metric to incorporate the network pathway structure in the analysis of spatial variability, and use a non-parametric technique to ascertain if the pattern in the empirical variogram is non-random.

A geostatistical approach for describing spatial pattern in stream networks

USGS Publications Warehouse

Ganio, L.M.; Torgersen, C.E.; Gresswell, R.E.

2005-01-01

The shape and configuration of branched networks influence ecological patterns and processes. Recent investigations of network influences in riverine ecology stress the need to quantify spatial structure not only in a two-dimensional plane, but also in networks. An initial step in understanding data from stream networks is discerning non-random patterns along the network. On the other hand, data collected in the network may be spatially autocorrelated and thus not suitable for traditional statistical analyses. Here we provide a method that uses commercially available software to construct an empirical variogram to describe spatial pattern in the relative abundance of coastal cutthroat trout in headwater stream networks. We describe the mathematical and practical considerations involved in calculating a variogram using a non-Euclidean distance metric to incorporate the network pathway structure in the analysis of spatial variability, and use a non-parametric technique to ascertain if the pattern in the empirical variogram is non-random.

Recovery of infrastructure networks after localised attacks

PubMed Central

Hu, Fuyu; Yeung, Chi Ho; Yang, Saini; Wang, Weiping; Zeng, An

2016-01-01

The stability of infrastructure network is always a critical issue studied by researchers in different fields. A lot of works have been devoted to reveal the robustness of the infrastructure networks against random and malicious attacks. However, real attack scenarios such as earthquakes and typhoons are instead localised attacks which are investigated only recently. Unlike previous studies, we examine in this paper the resilience of infrastructure networks by focusing on the recovery process from localised attacks. We introduce various preferential repair strategies and found that they facilitate and improve network recovery compared to that of random repairs, especially when population size is uneven at different locations. Moreover, our strategic repair methods show similar effectiveness as the greedy repair. The validations are conducted on simulated networks, and on real networks with real disasters. Our method is meaningful in practice as it can largely enhance network resilience and contribute to network risk reduction. PMID:27075559

Convergence and approximate calculation of average degree under different network sizes for decreasing random birth-and-death networks

NASA Astrophysics Data System (ADS)

Long, Yin; Zhang, Xiao-Jun; Wang, Kui

2018-05-01

In this paper, convergence and approximate calculation of average degree under different network sizes for decreasing random birth-and-death networks (RBDNs) are studied. First, we find and demonstrate that the average degree is convergent in the form of power law. Meanwhile, we discover that the ratios of the back items to front items of convergent reminder are independent of network link number for large network size, and we theoretically prove that the limit of the ratio is a constant. Moreover, since it is difficult to calculate the analytical solution of the average degree for large network sizes, we adopt numerical method to obtain approximate expression of the average degree to approximate its analytical solution. Finally, simulations are presented to verify our theoretical results.

Universal principles governing multiple random searchers on complex networks: The logarithmic growth pattern and the harmonic law

NASA Astrophysics Data System (ADS)

Weng, Tongfeng; Zhang, Jie; Small, Michael; Harandizadeh, Bahareh; Hui, Pan

2018-03-01

We propose a unified framework to evaluate and quantify the search time of multiple random searchers traversing independently and concurrently on complex networks. We find that the intriguing behaviors of multiple random searchers are governed by two basic principles—the logarithmic growth pattern and the harmonic law. Specifically, the logarithmic growth pattern characterizes how the search time increases with the number of targets, while the harmonic law explores how the search time of multiple random searchers varies relative to that needed by individual searchers. Numerical and theoretical results demonstrate these two universal principles established across a broad range of random search processes, including generic random walks, maximal entropy random walks, intermittent strategies, and persistent random walks. Our results reveal two fundamental principles governing the search time of multiple random searchers, which are expected to facilitate investigation of diverse dynamical processes like synchronization and spreading.

Physical layer one-time-pad data encryption through synchronized semiconductor laser networks

NASA Astrophysics Data System (ADS)

Argyris, Apostolos; Pikasis, Evangelos; Syvridis, Dimitris

2016-02-01

Semiconductor lasers (SL) have been proven to be a key device in the generation of ultrafast true random bit streams. Their potential to emit chaotic signals under conditions with desirable statistics, establish them as a low cost solution to cover various needs, from large volume key generation to real-time encrypted communications. Usually, only undemanding post-processing is needed to convert the acquired analog timeseries to digital sequences that pass all established tests of randomness. A novel architecture that can generate and exploit these true random sequences is through a fiber network in which the nodes are semiconductor lasers that are coupled and synchronized to central hub laser. In this work we show experimentally that laser nodes in such a star network topology can synchronize with each other through complex broadband signals that are the seed to true random bit sequences (TRBS) generated at several Gb/s. The potential for each node to access real-time generated and synchronized with the rest of the nodes random bit streams, through the fiber optic network, allows to implement an one-time-pad encryption protocol that mixes the synchronized true random bit sequence with real data at Gb/s rates. Forward-error correction methods are used to reduce the errors in the TRBS and the final error rate at the data decoding level. An appropriate selection in the sampling methodology and properties, as well as in the physical properties of the chaotic seed signal through which network locks in synchronization, allows an error free performance.

Modeling and optimization of Quality of Service routing in Mobile Ad hoc Networks

NASA Astrophysics Data System (ADS)

Rafsanjani, Marjan Kuchaki; Fatemidokht, Hamideh; Balas, Valentina Emilia

2016-01-01

Mobile ad hoc networks (MANETs) are a group of mobile nodes that are connected without using a fixed infrastructure. In these networks, nodes communicate with each other by forming a single-hop or multi-hop network. To design effective mobile ad hoc networks, it is important to evaluate the performance of multi-hop paths. In this paper, we present a mathematical model for a routing protocol under energy consumption and packet delivery ratio of multi-hop paths. In this model, we use geometric random graphs rather than random graphs. Our proposed model finds effective paths that minimize the energy consumption and maximizes the packet delivery ratio of the network. Validation of the mathematical model is performed through simulation.

Distributed Clone Detection in Static Wireless Sensor Networks: Random Walk with Network Division

PubMed Central

Khan, Wazir Zada; Aalsalem, Mohammed Y.; Saad, N. M.

2015-01-01

Wireless Sensor Networks (WSNs) are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND) for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s) by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads. PMID:25992913

Extreme events and event size fluctuations in biased random walks on networks.

PubMed

Kishore, Vimal; Santhanam, M S; Amritkar, R E

2012-05-01

Random walk on discrete lattice models is important to understand various types of transport processes. The extreme events, defined as exceedences of the flux of walkers above a prescribed threshold, have been studied recently in the context of complex networks. This was motivated by the occurrence of rare events such as traffic jams, floods, and power blackouts which take place on networks. In this work, we study extreme events in a generalized random walk model in which the walk is preferentially biased by the network topology. The walkers preferentially choose to hop toward the hubs or small degree nodes. In this setting, we show that extremely large fluctuations in event sizes are possible on small degree nodes when the walkers are biased toward the hubs. In particular, we obtain the distribution of event sizes on the network. Further, the probability for the occurrence of extreme events on any node in the network depends on its "generalized strength," a measure of the ability of a node to attract walkers. The generalized strength is a function of the degree of the node and that of its nearest neighbors. We obtain analytical and simulation results for the probability of occurrence of extreme events on the nodes of a network using a generalized random walk model. The result reveals that the nodes with a larger value of generalized strength, on average, display lower probability for the occurrence of extreme events compared to the nodes with lower values of generalized strength.

The Cognitive Social Network in Dreams: Transitivity, Assortativity, and Giant Component Proportion Are Monotonic.

PubMed

Han, Hye Joo; Schweickert, Richard; Xi, Zhuangzhuang; Viau-Quesnel, Charles

2016-04-01

For five individuals, a social network was constructed from a series of his or her dreams. Three important network measures were calculated for each network: transitivity, assortativity, and giant component proportion. These were monotonically related; over the five networks as transitivity increased, assortativity increased and giant component proportion decreased. The relations indicate that characters appear in dreams systematically. Systematicity likely arises from the dreamer's memory of people and their relations, which is from the dreamer's cognitive social network. But the dream social network is not a copy of the cognitive social network. Waking life social networks tend to have positive assortativity; that is, people tend to be connected to others with similar connectivity. Instead, in our sample of dream social networks assortativity is more often negative or near 0, as in online social networks. We show that if characters appear via a random walk, negative assortativity can result, particularly if the random walk is biased as suggested by remote associations. Copyright © 2015 Cognitive Science Society, Inc.

Changing climates of conflict: A social network experiment in 56 schools.

PubMed

Paluck, Elizabeth Levy; Shepherd, Hana; Aronow, Peter M

2016-01-19

Theories of human behavior suggest that individuals attend to the behavior of certain people in their community to understand what is socially normative and adjust their own behavior in response. An experiment tested these theories by randomizing an anticonflict intervention across 56 schools with 24,191 students. After comprehensively measuring every school's social network, randomly selected seed groups of 20-32 students from randomly selected schools were assigned to an intervention that encouraged their public stance against conflict at school. Compared with control schools, disciplinary reports of student conflict at treatment schools were reduced by 30% over 1 year. The effect was stronger when the seed group contained more "social referent" students who, as network measures reveal, attract more student attention. Network analyses of peer-to-peer influence show that social referents spread perceptions of conflict as less socially normative.

Understanding regulatory networks requires more than computing a multitude of graph statistics. Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O.C. Martin et al.

NASA Astrophysics Data System (ADS)

Tkačik, Gašper

2016-07-01

The article by O. Martin and colleagues provides a much needed systematic review of a body of work that relates the topological structure of genetic regulatory networks to evolutionary selection for function. This connection is very important. Using the current wealth of genomic data, statistical features of regulatory networks (e.g., degree distributions, motif composition, etc.) can be quantified rather easily; it is, however, often unclear how to interpret the results. On a graph theoretic level the statistical significance of the results can be evaluated by comparing observed graphs to ;randomized; ones (bravely ignoring the issue of how precisely to randomize!) and comparing the frequency of appearance of a particular network structure relative to a randomized null expectation. While this is a convenient operational test for statistical significance, its biological meaning is questionable. In contrast, an in-silico genotype-to-phenotype model makes explicit the assumptions about the network function, and thus clearly defines the expected network structures that can be compared to the case of no selection for function and, ultimately, to data.

A Bell inequality for a class of multilocal ring networks

NASA Astrophysics Data System (ADS)

Frey, Michael

2017-11-01

Quantum networks with independent sources of entanglement (hidden variables) and nodes that execute joint quantum measurements can create strong quantum correlations spanning the breadth of the network. Understanding of these correlations has to the present been limited to standard Bell experiments with one source of shared randomness, bilocal arrangements having two local sources of shared randomness, and multilocal networks with tree topologies. We introduce here a class of quantum networks with ring topologies comprised of subsystems each with its own internally shared source of randomness. We prove a Bell inequality for these networks, and to demonstrate violations of this inequality, we focus on ring networks with three-qubit subsystems. Three qubits are capable of two non-equivalent types of entanglement, GHZ and W-type. For rings of any number N of three-qubit subsystems, our inequality is violated when the subsystems are each internally GHZ-entangled. This violation is consistently stronger when N is even. This quantitative even-odd difference for GHZ entanglement becomes extreme in the case of W-type entanglement. When the ring size N is even, the presence of W-type entanglement is successfully detected; when N is odd, the inequality consistently fails to detect its presence.

Theory of rumour spreading in complex social networks

NASA Astrophysics Data System (ADS)

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

2007-01-01

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

Mathematical modelling of complex contagion on clustered networks

NASA Astrophysics Data System (ADS)

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

2015-09-01

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

Interference Drop Scheme: Enhancing QoS Provision in Multi-Hop Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Luo, Chang-Yi; Komuro, Nobuyoshi; Takahashi, Kiyoshi; Kasai, Hiroyuki; Ueda, Hiromi; Tsuboi, Toshinori

Ad hoc networking uses wireless technologies to construct networks with no physical infrastructure and so are expected to provide instant networking in areas such as disaster recovery sites and inter-vehicle communication. Unlike conventional wired networks services, services in ad hoc networks are easily disrupted by the frequent changes in traffic and topology. Therefore, solutions to assure the Quality of Services (QoS) in ad hoc networks are different from the conventional ones used in wired networks. In this paper, we propose a new queue management scheme, Interference Drop Scheme (IDS) for ad hoc networks. In the conventional queue management approaches such as FIFO (First-in First-out) and RED (Random Early Detection), a queue is usually managed by a queue length limit. FIFO discards packets according to the queue limit, and RED discards packets in an early and random fashion. IDS, on the other hand, manages the queue according to wireless interference time, which increases as the number of contentions in the MAC layer increases. When there are many MAC contentions, IDS discards TCP data packets. By observing the interference time and discarding TCP data packets, our simulation results show that IDS improves TCP performance and reduces QoS violations in UDP in ad hoc networks with chain, grid, and random topologies. Our simulation results also demonstrate that wireless interference time is a better metric than queue length limit for queue management in multi-hop ad hoc networks.

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

PubMed

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

2016-01-01

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

Extracting the field-effect mobilities of random semiconducting single-walled carbon nanotube networks: A critical comparison of methods

NASA Astrophysics Data System (ADS)

Schießl, Stefan P.; Rother, Marcel; Lüttgens, Jan; Zaumseil, Jana

2017-11-01

The field-effect mobility is an important figure of merit for semiconductors such as random networks of single-walled carbon nanotubes (SWNTs). However, owing to their network properties and quantum capacitance, the standard models for field-effect transistors cannot be applied without modifications. Several different methods are used to determine the mobility with often very different results. We fabricated and characterized field-effect transistors with different polymer-sorted, semiconducting SWNT network densities ranging from low (≈6 μm-1) to densely packed quasi-monolayers (≈26 μm-1) with a maximum on-conductance of 0.24 μS μm-1 and compared four different techniques to evaluate the field-effect mobility. We demonstrate the limits and requirements for each method with regard to device layout and carrier accumulation. We find that techniques that take into account the measured capacitance on the active device give the most reliable mobility values. Finally, we compare our experimental results to a random-resistor-network model.

Parallel Algorithms for Switching Edges in Heterogeneous Graphs.

PubMed

Bhuiyan, Hasanuzzaman; Khan, Maleq; Chen, Jiangzhuo; Marathe, Madhav

2017-06-01

An edge switch is an operation on a graph (or network) where two edges are selected randomly and one of their end vertices are swapped with each other. Edge switch operations have important applications in graph theory and network analysis, such as in generating random networks with a given degree sequence, modeling and analyzing dynamic networks, and in studying various dynamic phenomena over a network. The recent growth of real-world networks motivates the need for efficient parallel algorithms. The dependencies among successive edge switch operations and the requirement to keep the graph simple (i.e., no self-loops or parallel edges) as the edges are switched lead to significant challenges in designing a parallel algorithm. Addressing these challenges requires complex synchronization and communication among the processors leading to difficulties in achieving a good speedup by parallelization. In this paper, we present distributed memory parallel algorithms for switching edges in massive networks. These algorithms provide good speedup and scale well to a large number of processors. A harmonic mean speedup of 73.25 is achieved on eight different networks with 1024 processors. One of the steps in our edge switch algorithms requires the computation of multinomial random variables in parallel. This paper presents the first non-trivial parallel algorithm for the problem, achieving a speedup of 925 using 1024 processors.

Parallel Algorithms for Switching Edges in Heterogeneous Graphs☆

PubMed Central

Khan, Maleq; Chen, Jiangzhuo; Marathe, Madhav

2017-01-01

An edge switch is an operation on a graph (or network) where two edges are selected randomly and one of their end vertices are swapped with each other. Edge switch operations have important applications in graph theory and network analysis, such as in generating random networks with a given degree sequence, modeling and analyzing dynamic networks, and in studying various dynamic phenomena over a network. The recent growth of real-world networks motivates the need for efficient parallel algorithms. The dependencies among successive edge switch operations and the requirement to keep the graph simple (i.e., no self-loops or parallel edges) as the edges are switched lead to significant challenges in designing a parallel algorithm. Addressing these challenges requires complex synchronization and communication among the processors leading to difficulties in achieving a good speedup by parallelization. In this paper, we present distributed memory parallel algorithms for switching edges in massive networks. These algorithms provide good speedup and scale well to a large number of processors. A harmonic mean speedup of 73.25 is achieved on eight different networks with 1024 processors. One of the steps in our edge switch algorithms requires the computation of multinomial random variables in parallel. This paper presents the first non-trivial parallel algorithm for the problem, achieving a speedup of 925 using 1024 processors. PMID:28757680

Egg Parasitoids from Pakistan as possible classical biological control agents of the invasive pest, Bagrada hilaris (Heteroptera: Pentatomidae)

USDA-ARS?s Scientific Manuscript database

The newly invasive pest stink bug, Bagrada hilaris, threatens the cole crop industry and certain ornamentals in the U.S. Without its co-evolved natural enemies, it is likely to spread from the Southwest U.S. to the east coast, requiring millions more dollars to control it. If key biological control ...

Variables, Decisions, and Scripting in Construct

DTIC Science & Technology

2009-09-01

grounded in sociology and cognitive science which seeks to model the processes and situations by which humans interact and share information...Construct is an embodiment of constructuralism (Carley 1986), a theory which posits that human social structures and cognitive structures co-evolve so that...human cognition reflects human social behavior, and that human social behavior simultaneously influences cognitive processes. Recent work with

Douglas-fir Progeny Testing for Resistance to Western Spruce Budworm

Treesearch

Geral I. McDonald

1983-01-01

Ample evidence exists that inland populations of Douglas-fir suffer varying amounts of defoliation by western spruce budworm (Johnson and Denton 1975; Williams 1967; McDonald 1981). Such variation in plant insect association can be the result of the plant escaping attack in time and place to actual confrontation between plant and insect (Harris 1980). Co-evolved...

Unravelling and managing fusiform rust disease: a model approach for coevolved forest tree pathosystems.

Treesearch

CD Nelson; TL Kubisiak; HV Amerson

2010-01-01

Fusiform rust disease remains the most destructive disease in pine plantations in the southern United States. Our ongoing research is designed to identify, map, and clone the interacting genes of the host and pathogen. Several resistance (R) genes have been identified and genetically mapped using informative pine families and single-spore isolate inoculations. In...

Structural Determinants of Sleeping Beauty Transposase Activity

PubMed Central

Abrusán, György; Yant, Stephen R; Szilágyi, András; Marsh, Joseph A; Mátés, Lajos; Izsvák, Zsuzsanna; Barabás, Orsolya; Ivics, Zoltán

2016-01-01

Transposases are important tools in genome engineering, and there is considerable interest in engineering more efficient ones. Here, we seek to understand the factors determining their activity using the Sleeping Beauty transposase. Recent work suggests that protein coevolutionary information can be used to classify groups of physically connected, coevolving residues into elements called “sectors”, which have proven useful for understanding the folding, allosteric interactions, and enzymatic activity of proteins. Using extensive mutagenesis data, protein modeling and analysis of folding energies, we show that (i) The Sleeping Beauty transposase contains two sectors, which span across conserved domains, and are enriched in DNA-binding residues, indicating that the DNA binding and endonuclease functions of the transposase coevolve; (ii) Sector residues are highly sensitive to mutations, and most mutations of these residues strongly reduce transposition rate; (iii) Mutations with a strong effect on free energy of folding in the DDE domain of the transposase significantly reduce transposition rate. (iv) Mutations that influence DNA and protein-protein interactions generally reduce transposition rate, although most hyperactive mutants are also located on the protein surface, including residues with protein-protein interactions. This suggests that hyperactivity results from the modification of protein interactions, rather than the stabilization of protein fold. PMID:27401040

Coevolving memetic algorithms: a review and progress report.

PubMed

Smith, Jim E

2007-02-01

Coevolving memetic algorithms are a family of metaheuristic search algorithms in which a rule-based representation of local search (LS) is coadapted alongside candidate solutions within a hybrid evolutionary system. Simple versions of these systems have been shown to outperform other nonadaptive memetic and evolutionary algorithms on a range of problems. This paper presents a rationale for such systems and places them in the context of other recent work on adaptive memetic algorithms. It then proposes a general structure within which a population of LS algorithms can be evolved in tandem with the solutions to which they are applied. Previous research started with a simple self-adaptive system before moving on to more complex models. Results showed that the algorithm was able to discover and exploit certain forms of structure and regularities within the problems. This "metalearning" of problem features provided a means of creating highly scalable algorithms. This work is briefly reviewed to highlight some of the important findings and behaviors exhibited. Based on this analysis, new results are then presented from systems with more flexible representations, which, again, show significant improvements. Finally, the current state of, and future directions for, research in this area is discussed.

Evolution, human-microbe interactions, and life history plasticity.

PubMed

Rook, Graham; Bäckhed, Fredrik; Levin, Bruce R; McFall-Ngai, Margaret J; McLean, Angela R

2017-07-29

A bacterium was once a component of the ancestor of all eukaryotic cells, and much of the human genome originated in microorganisms. Today, all vertebrates harbour large communities of microorganisms (microbiota), particularly in the gut, and at least 20% of the small molecules in human blood are products of the microbiota. Changing human lifestyles and medical practices are disturbing the content and diversity of the microbiota, while simultaneously reducing our exposures to the so-called old infections and to organisms from the natural environment with which human beings co-evolved. Meanwhile, population growth is increasing the exposure of human beings to novel pathogens, particularly the crowd infections that were not part of our evolutionary history. Thus some microbes have co-evolved with human beings and play crucial roles in our physiology and metabolism, whereas others are entirely intrusive. Human metabolism is therefore a tug-of-war between managing beneficial microbes, excluding detrimental ones, and channelling as much energy as is available into other essential functions (eg, growth, maintenance, reproduction). This tug-of-war shapes the passage of each individual through life history decision nodes (eg, how fast to grow, when to mature, and how long to live). Copyright © 2017 Elsevier Ltd. All rights reserved.

The Role of the Environment in the Evolution of Tolerance and Resistance to a Pathogen.

PubMed

Zeller, Michael; Koella, Jacob C

2017-09-01

Defense against parasites can be divided into resistance, which limits parasite burden, and tolerance, which reduces pathogenesis at a given parasite burden. Distinguishing between the two and understanding which defense is favored by evolution in different ecological settings are important, as they lead to fundamentally different evolutionary trajectories of host-parasite interactions. We let the mosquito Aedes aegypti evolve under different food levels and with either no parasite, a constant parasite, or a coevolving parasite (the microsporidian Vavraia culicis). We then tested tolerance and resistance of the evolved lines on a population level at the two food levels. Exposure to parasites during evolution increased resistance and tolerance, but there were no differences between the lines evolved with coevolving or constant parasites. Mosquitoes that had evolved with food restriction had higher resistance than those evolved with high food but similar tolerance. The mosquitoes that had restricted food when being tested had lower tolerance than those with normal food, but there was no difference in resistance. Our results emphasize the complexity and dependence on environmental conditions of the evolution and expression of resistance and tolerance and help to evaluate some of the predictions about the evolution of host defense against parasites.

Goldilocks and the three inorganic equilibria: how Earth's chemistry and life coevolve to be nearly in tune.

PubMed

Rickaby, R E M

2015-03-13

Life and the chemical environment are united in an inescapable feedback cycle. The periodic table of the elements essential for life has transformed over Earth's history, but, as today, evolved in tune with the elements available in abundance in the environment. The most revolutionary time in life's history was the advent and proliferation of oxygenic photosynthesis which forced the environment towards a greater degree of oxidation. Consideration of three inorganic chemical equilibria throughout this gradual oxygenation prescribes a phased release of trace metals to the environment, which appear to have coevolved with employment of these new chemicals by life. Evolution towards complexity was chemically constrained, and changes in availability of notably Fe, Zn and Cu paced the systematic development of complex organisms. Evolving life repeatedly catalysed its own chemical challenges via the unwitting release of new and initially toxic chemicals. Ultimately, the harnessing of these allowed life to advance to greater complexity, though the mechanism responsible for translating novel chemistry to heritable use remains elusive. Whether a chemical acts as a poison or a nutrient lies both in the dose and in its environmental history. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Efficient randomization of biological networks while preserving functional characterization of individual nodes.

PubMed

Iorio, Francesco; Bernardo-Faura, Marti; Gobbi, Andrea; Cokelaer, Thomas; Jurman, Giuseppe; Saez-Rodriguez, Julio

2016-12-20

Networks are popular and powerful tools to describe and model biological processes. Many computational methods have been developed to infer biological networks from literature, high-throughput experiments, and combinations of both. Additionally, a wide range of tools has been developed to map experimental data onto reference biological networks, in order to extract meaningful modules. Many of these methods assess results' significance against null distributions of randomized networks. However, these standard unconstrained randomizations do not preserve the functional characterization of the nodes in the reference networks (i.e. their degrees and connection signs), hence including potential biases in the assessment. Building on our previous work about rewiring bipartite networks, we propose a method for rewiring any type of unweighted networks. In particular we formally demonstrate that the problem of rewiring a signed and directed network preserving its functional connectivity (F-rewiring) reduces to the problem of rewiring two induced bipartite networks. Additionally, we reformulate the lower bound to the iterations' number of the switching-algorithm to make it suitable for the F-rewiring of networks of any size. Finally, we present BiRewire3, an open-source Bioconductor package enabling the F-rewiring of any type of unweighted network. We illustrate its application to a case study about the identification of modules from gene expression data mapped on protein interaction networks, and a second one focused on building logic models from more complex signed-directed reference signaling networks and phosphoproteomic data. BiRewire3 it is freely available at https://www.bioconductor.org/packages/BiRewire/ , and it should have a broad application as it allows an efficient and analytically derived statistical assessment of results from any network biology tool.

A New Framework for Analysis of Coevolutionary Systems-Directed Graph Representation and Random Walks.

PubMed

Chong, Siang Yew; Tiňo, Peter; He, Jun; Yao, Xin

2017-11-20

Studying coevolutionary systems in the context of simplified models (i.e., games with pairwise interactions between coevolving solutions modeled as self plays) remains an open challenge since the rich underlying structures associated with pairwise-comparison-based fitness measures are often not taken fully into account. Although cyclic dynamics have been demonstrated in several contexts (such as intransitivity in coevolutionary problems), there is no complete characterization of cycle structures and their effects on coevolutionary search. We develop a new framework to address this issue. At the core of our approach is the directed graph (digraph) representation of coevolutionary problems that fully captures structures in the relations between candidate solutions. Coevolutionary processes are modeled as a specific type of Markov chains-random walks on digraphs. Using this framework, we show that coevolutionary problems admit a qualitative characterization: a coevolutionary problem is either solvable (there is a subset of solutions that dominates the remaining candidate solutions) or not. This has an implication on coevolutionary search. We further develop our framework that provides the means to construct quantitative tools for analysis of coevolutionary processes and demonstrate their applications through case studies. We show that coevolution of solvable problems corresponds to an absorbing Markov chain for which we can compute the expected hitting time of the absorbing class. Otherwise, coevolution will cycle indefinitely and the quantity of interest will be the limiting invariant distribution of the Markov chain. We also provide an index for characterizing complexity in coevolutionary problems and show how they can be generated in a controlled manner.

Characterizing short-term stability for Boolean networks over any distribution of transfer functions

DOE PAGES

Seshadhri, C.; Smith, Andrew M.; Vorobeychik, Yevgeniy; ...

2016-07-05

Here we present a characterization of short-term stability of random Boolean networks under arbitrary distributions of transfer functions. Given any distribution of transfer functions for a random Boolean network, we present a formula that decides whether short-term chaos (damage spreading) will happen. We provide a formal proof for this formula, and empirically show that its predictions are accurate. Previous work only works for special cases of balanced families. Finally, it has been observed that these characterizations fail for unbalanced families, yet such families are widespread in real biological networks.

Modeling of contact tracing in social networks

NASA Astrophysics Data System (ADS)

Tsimring, Lev S.; Huerta, Ramón

2003-07-01

Spreading of certain infections in complex networks is effectively suppressed by using intelligent strategies for epidemic control. One such standard epidemiological strategy consists in tracing contacts of infected individuals. In this paper, we use a recently introduced generalization of the standard susceptible-infectious-removed stochastic model for epidemics in sparse random networks which incorporates an additional (traced) state. We describe a deterministic mean-field description which yields quantitative agreement with stochastic simulations on random graphs. We also discuss the role of contact tracing in epidemics control in small-world and scale-free networks. Effectiveness of contact tracing grows as the rewiring probability is reduced.

On the strength of random fiber networks

NASA Astrophysics Data System (ADS)

Deogekar, S.; Picu, R. C.

2018-07-01

Damage accumulation and failure in random fiber networks is of importance in a variety of applications, from design of synthetic materials, such as paper and non-wovens, to accidental tearing of biological tissues. In this work we study these processes using three-dimensional models of athermal fiber networks, focusing attention on the modes of failure and on the relationship between network strength and network structural parameters. We consider network failure at small and large strains associated with the rupture of inter-fiber bonds. It is observed that the strength increases linearly with the network volume fraction and with the bond strength, while the stretch at peak stress is inversely related to these two parameters. A small fraction of the bonds rupture before peak stress and this fraction increases with increasing failure stretch. Rendering the bond strength stochastic causes a reduction of the network strength. However, heterogeneity retards damage localization and increases the stretch at peak stress, therefore promoting ductility.

Network Structure and the Risk for HIV Transmission Among Rural Drug Users

PubMed Central

Young, A. M.; Jonas, A. B.; Mullins, U. L.; Halgin, D. S.

2012-01-01

Research suggests that structural properties of drug users’ social networks can have substantial effects on HIV risk. The purpose of this study was to investigate if the structural properties of Appalachian drug users’ risk networks could lend insight into the potential for HIV transmission in this population. Data from 503 drug users recruited through respondent-driven sampling were used to construct a sociometric risk network. Network ties represented relationships in which partners had engaged in unprotected sex and/or shared injection equipment. Compared to 1,000 randomly generated networks, the observed network was found to have a larger main component and exhibit more cohesiveness and centralization than would be expected at random. Thus, the risk network structure in this sample has many structural characteristics shown to be facilitative of HIV transmission. This underscores the importance of primary prevention in this population and prompts further investigation into the epidemiology of HIV in the region. PMID:23184464

Analysis of complex network performance and heuristic node removal strategies

NASA Astrophysics Data System (ADS)

Jahanpour, Ehsan; Chen, Xin

2013-12-01

Removing important nodes from complex networks is a great challenge in fighting against criminal organizations and preventing disease outbreaks. Six network performance metrics, including four new metrics, are applied to quantify networks' diffusion speed, diffusion scale, homogeneity, and diameter. In order to efficiently identify nodes whose removal maximally destroys a network, i.e., minimizes network performance, ten structured heuristic node removal strategies are designed using different node centrality metrics including degree, betweenness, reciprocal closeness, complement-derived closeness, and eigenvector centrality. These strategies are applied to remove nodes from the September 11, 2001 hijackers' network, and their performance are compared to that of a random strategy, which removes randomly selected nodes, and the locally optimal solution (LOS), which removes nodes to minimize network performance at each step. The computational complexity of the 11 strategies and LOS is also analyzed. Results show that the node removal strategies using degree and betweenness centralities are more efficient than other strategies.

Random walks on activity-driven networks with attractiveness

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

Emergence of cooperation in non-scale-free networks

NASA Astrophysics Data System (ADS)

Zhang, Yichao; Aziz-Alaoui, M. A.; Bertelle, Cyrille; Zhou, Shi; Wang, Wenting

2014-06-01

Evolutionary game theory is one of the key paradigms behind many scientific disciplines from science to engineering. Previous studies proposed a strategy updating mechanism, which successfully demonstrated that the scale-free network can provide a framework for the emergence of cooperation. Instead, individuals in random graphs and small-world networks do not favor cooperation under this updating rule. However, a recent empirical result shows the heterogeneous networks do not promote cooperation when humans play a prisoner’s dilemma. In this paper, we propose a strategy updating rule with payoff memory. We observe that the random graphs and small-world networks can provide even better frameworks for cooperation than the scale-free networks in this scenario. Our observations suggest that the degree heterogeneity may be neither a sufficient condition nor a necessary condition for the widespread cooperation in complex networks. Also, the topological structures are not sufficed to determine the level of cooperation in complex networks.

Quantifying networks complexity from information geometry viewpoint

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

Felice, Domenico, E-mail: domenico.felice@unicam.it; Mancini, Stefano; INFN-Sezione di Perugia, Via A. Pascoli, I-06123 Perugia

We consider a Gaussian statistical model whose parameter space is given by the variances of random variables. Underlying this model we identify networks by interpreting random variables as sitting on vertices and their correlations as weighted edges among vertices. We then associate to the parameter space a statistical manifold endowed with a Riemannian metric structure (that of Fisher-Rao). Going on, in analogy with the microcanonical definition of entropy in Statistical Mechanics, we introduce an entropic measure of networks complexity. We prove that it is invariant under networks isomorphism. Above all, considering networks as simplicial complexes, we evaluate this entropy onmore » simplexes and find that it monotonically increases with their dimension.« less

Why we need more than just randomized controlled trials to establish the effectiveness of online social networks for health behavior change.

PubMed

Vandelanotte, Corneel; Maher, Carol A

2015-01-01

Despite their popularity and potential to promote health in large populations, the effectiveness of online social networks (e.g., Facebook) to improve health behaviors has been somewhat disappointing. Most of the research examining the effectiveness of such interventions has used randomized controlled trials (RCTs). It is asserted that the modest outcomes may be due to characteristics specific to both online social networks and RCTs. The highly controlled nature of RCTs stifles the dynamic nature of online social networks. Alternative and ecologically valid research designs that evaluate online social networks in real-life conditions are needed to advance the science in this area.

Immunization of complex networks

NASA Astrophysics Data System (ADS)

Pastor-Satorras, Romualdo; Vespignani, Alessandro

2002-03-01

Complex networks such as the sexual partnership web or the Internet often show a high degree of redundancy and heterogeneity in their connectivity properties. This peculiar connectivity provides an ideal environment for the spreading of infective agents. Here we show that the random uniform immunization of individuals does not lead to the eradication of infections in all complex networks. Namely, networks with scale-free properties do not acquire global immunity from major epidemic outbreaks even in the presence of unrealistically high densities of randomly immunized individuals. The absence of any critical immunization threshold is due to the unbounded connectivity fluctuations of scale-free networks. Successful immunization strategies can be developed only by taking into account the inhomogeneous connectivity properties of scale-free networks. In particular, targeted immunization schemes, based on the nodes' connectivity hierarchy, sharply lower the network's vulnerability to epidemic attacks.

Enhanced networked server management with random remote backups

NASA Astrophysics Data System (ADS)

Kim, Song-Kyoo

2003-08-01

In this paper, the model is focused on available server management in network environments. The (remote) backup servers are hooked up by VPN (Virtual Private Network) and replace broken main severs immediately. A virtual private network (VPN) is a way to use a public network infrastructure and hooks up long-distance servers within a single network infrastructure. The servers can be represent as "machines" and then the system deals with main unreliable and random auxiliary spare (remote backup) machines. When the system performs a mandatory routine maintenance, auxiliary machines are being used for backups during idle periods. Unlike other existing models, the availability of auxiliary machines is changed for each activation in this enhanced model. Analytically tractable results are obtained by using several mathematical techniques and the results are demonstrated in the framework of optimized networked server allocation problems.

OSI Network-layer Abstraction: Analysis of Simulation Dynamics and Performance Indicators

NASA Astrophysics Data System (ADS)

Lawniczak, Anna T.; Gerisch, Alf; Di Stefano, Bruno

2005-06-01

The Open Systems Interconnection (OSI) reference model provides a conceptual framework for communication among computers in a data communication network. The Network Layer of this model is responsible for the routing and forwarding of packets of data. We investigate the OSI Network Layer and develop an abstraction suitable for the study of various network performance indicators, e.g. throughput, average packet delay, average packet speed, average packet path-length, etc. We investigate how the network dynamics and the network performance indicators are affected by various routing algorithms and by the addition of randomly generated links into a regular network connection topology of fixed size. We observe that the network dynamics is not simply the sum of effects resulting from adding individual links to the connection topology but rather is governed nonlinearly by the complex interactions caused by the existence of all randomly added and already existing links in the network. Data for our study was gathered using Netzwerk-1, a C++ simulation tool that we developed for our abstraction.

PUFKEY: A High-Security and High-Throughput Hardware True Random Number Generator for Sensor Networks

PubMed Central

Li, Dongfang; Lu, Zhaojun; Zou, Xuecheng; Liu, Zhenglin

2015-01-01

Random number generators (RNG) play an important role in many sensor network systems and applications, such as those requiring secure and robust communications. In this paper, we develop a high-security and high-throughput hardware true random number generator, called PUFKEY, which consists of two kinds of physical unclonable function (PUF) elements. Combined with a conditioning algorithm, true random seeds are extracted from the noise on the start-up pattern of SRAM memories. These true random seeds contain full entropy. Then, the true random seeds are used as the input for a non-deterministic hardware RNG to generate a stream of true random bits with a throughput as high as 803 Mbps. The experimental results show that the bitstream generated by the proposed PUFKEY can pass all standard national institute of standards and technology (NIST) randomness tests and is resilient to a wide range of security attacks. PMID:26501283

PUFKEY: a high-security and high-throughput hardware true random number generator for sensor networks.

PubMed

Li, Dongfang; Lu, Zhaojun; Zou, Xuecheng; Liu, Zhenglin

2015-10-16

Random number generators (RNG) play an important role in many sensor network systems and applications, such as those requiring secure and robust communications. In this paper, we develop a high-security and high-throughput hardware true random number generator, called PUFKEY, which consists of two kinds of physical unclonable function (PUF) elements. Combined with a conditioning algorithm, true random seeds are extracted from the noise on the start-up pattern of SRAM memories. These true random seeds contain full entropy. Then, the true random seeds are used as the input for a non-deterministic hardware RNG to generate a stream of true random bits with a throughput as high as 803 Mbps. The experimental results show that the bitstream generated by the proposed PUFKEY can pass all standard national institute of standards and technology (NIST) randomness tests and is resilient to a wide range of security attacks.

An ecological mechanism to create regular patterns of surface dissolution in a low-relief carbonate landscape

NASA Astrophysics Data System (ADS)

Cohen, M. J.; Martin, J. B.; Mclaughlin, D. L.; Osborne, T.; Murray, A.; Watts, A. C.; Watts, D.; Heffernan, J. B.

2012-12-01

Development of karst landscapes is controlled by focused delivery of water undersaturated with respect to the soluble rock minerals. As that water comes to equilibrium with the rock, secondary porosity is incrementally reinforced creating a positive feedback that acts to augment the drainage network and subsequent water delivery. In most self-organizing systems, spatial positive feedbacks create features (in landscapes: patches; in karst aquifers: conduits) whose size-frequency relationship follows a power function, indicating a higher probability of large features than would occur with a random or Gaussian genesis process. Power functions describe several aspects of secondary porosity in the Upper Floridan Aquifer in north Florida. In contrast, a different pattern arises in the karst landscape in southwest Florida (Big Cypress National Preserve; BICY), where low-relief and a shallow aquiclude govern regional hydrology. There, the landscape pattern is highly regular (Fig. 1), with circular cypress-dominated wetlands occupying depressions that are hydrologically isolated and distributed evenly in a matrix of pine uplands. Regular landscape patterning results from spatially coupled feedbacks, one positive operating locally that expands patches coupled to another negative that operates at distance, eventually inhibiting patch expansion. The positive feedback in BICY is thought to derive from the presence of surface depressions, which sustain prolonged inundation in this low-relief setting, and facilitate wetland development that greatly augments dissolution potential of infiltrating water in response to ecosystem metabolic processes. In short, wetlands "drill" into the carbonate leading to both vertical and lateral basin expansion. Wetland expansion occurs at the expense of surrounding upland area, which is the local catchment that subsidizes water availability. A distal inhibitory feedback on basin expansion thus occurs as the water necessary to sustain prolonged inundation becomes limiting. The implied strong reciprocal coupling between surface production of organic matter and patterns of induced subsurface carbonate dissolution are a novel example of co-evolving biogeomorphic processes in the earth system. Fig. 1 - Regular patterned landscape in Big Cypress National Preserve showing cypress dominated wetlands (round features) embedded in a mosaic of pine and grass uplands. Exposed carbonate rings are evident at the margins of many of the wetland basins.

Distributed Synchronization in Networks of Agent Systems With Nonlinearities and Random Switchings.

PubMed

Tang, Yang; Gao, Huijun; Zou, Wei; Kurths, Jürgen

2013-02-01

In this paper, the distributed synchronization problem of networks of agent systems with controllers and nonlinearities subject to Bernoulli switchings is investigated. Controllers and adaptive updating laws injected in each vertex of networks depend on the state information of its neighborhood. Three sets of Bernoulli stochastic variables are introduced to describe the occurrence probabilities of distributed adaptive controllers, updating laws and nonlinearities, respectively. By the Lyapunov functions method, we show that the distributed synchronization of networks composed of agent systems with multiple randomly occurring nonlinearities, multiple randomly occurring controllers, and multiple randomly occurring updating laws can be achieved in mean square under certain criteria. The conditions derived in this paper can be solved by semi-definite programming. Moreover, by mathematical analysis, we find that the coupling strength, the probabilities of the Bernoulli stochastic variables, and the form of nonlinearities have great impacts on the convergence speed and the terminal control strength. The synchronization criteria and the observed phenomena are demonstrated by several numerical simulation examples. In addition, the advantage of distributed adaptive controllers over conventional adaptive controllers is illustrated.

Naming games in two-dimensional and small-world-connected random geometric networks.

PubMed

Lu, Qiming; Korniss, G; Szymanski, B K

2008-01-01

We investigate a prototypical agent-based model, the naming game, on two-dimensional random geometric networks. The naming game [Baronchelli, J. Stat. Mech.: Theory Exp. (2006) P06014] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the naming games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long-range communication links are added on top of the random geometric graph, resulting in a "small-world"-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case.

Sequential defense against random and intentional attacks in complex networks.

PubMed

Chen, Pin-Yu; Cheng, Shin-Ming

2015-02-01

Network robustness against attacks is one of the most fundamental researches in network science as it is closely associated with the reliability and functionality of various networking paradigms. However, despite the study on intrinsic topological vulnerabilities to node removals, little is known on the network robustness when network defense mechanisms are implemented, especially for networked engineering systems equipped with detection capabilities. In this paper, a sequential defense mechanism is first proposed in complex networks for attack inference and vulnerability assessment, where the data fusion center sequentially infers the presence of an attack based on the binary attack status reported from the nodes in the network. The network robustness is evaluated in terms of the ability to identify the attack prior to network disruption under two major attack schemes, i.e., random and intentional attacks. We provide a parametric plug-in model for performance evaluation on the proposed mechanism and validate its effectiveness and reliability via canonical complex network models and real-world large-scale network topology. The results show that the sequential defense mechanism greatly improves the network robustness and mitigates the possibility of network disruption by acquiring limited attack status information from a small subset of nodes in the network.

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

Changing climates of conflict: A social network experiment in 56 schools

PubMed Central

Paluck, Elizabeth Levy; Shepherd, Hana; Aronow, Peter M.

2016-01-01

Theories of human behavior suggest that individuals attend to the behavior of certain people in their community to understand what is socially normative and adjust their own behavior in response. An experiment tested these theories by randomizing an anticonflict intervention across 56 schools with 24,191 students. After comprehensively measuring every school’s social network, randomly selected seed groups of 20–32 students from randomly selected schools were assigned to an intervention that encouraged their public stance against conflict at school. Compared with control schools, disciplinary reports of student conflict at treatment schools were reduced by 30% over 1 year. The effect was stronger when the seed group contained more “social referent” students who, as network measures reveal, attract more student attention. Network analyses of peer-to-peer influence show that social referents spread perceptions of conflict as less socially normative. PMID:26729884

Discrete gravity on random tensor network and holographic Rényi entropy

NASA Astrophysics Data System (ADS)

Han, Muxin; Huang, Shilin

2017-11-01

In this paper we apply the discrete gravity and Regge calculus to tensor networks and Anti-de Sitter/conformal field theory (AdS/CFT) correspondence. We construct the boundary many-body quantum state |Ψ〉 using random tensor networks as the holographic mapping, applied to the Wheeler-deWitt wave function of bulk Euclidean discrete gravity in 3 dimensions. The entanglement Rényi entropy of |Ψ〉 is shown to holographically relate to the on-shell action of Einstein gravity on a branch cover bulk manifold. The resulting Rényi entropy S n of |Ψ〉 approximates with high precision the Rényi entropy of ground state in 2-dimensional conformal field theory (CFT). In particular it reproduces the correct n dependence. Our results develop the framework of realizing the AdS3/CFT2 correspondence on random tensor networks, and provide a new proposal to approximate the CFT ground state.

Correcting Evaluation Bias of Relational Classifiers with Network Cross Validation

DTIC Science & Technology

2010-01-01

classi- fication algorithms: simple random resampling (RRS), equal-instance random resampling (ERS), and network cross-validation ( NCV ). The first two... NCV procedure that eliminates overlap between test sets altogether. The procedure samples for k disjoint test sets that will be used for evaluation...propLabeled ∗ S) nodes from train Pool in f erenceSet =network − trainSet F = F ∪ < trainSet, test Set, in f erenceSet > end for output: F NCV addresses

Structure of a randomly grown 2-d network.

PubMed

Ajazi, Fioralba; Napolitano, George M; Turova, Tatyana; Zaurbek, Izbassar

2015-10-01

We introduce a growing random network on a plane as a model of a growing neuronal network. The properties of the structure of the induced graph are derived. We compare our results with available data. In particular, it is shown that depending on the parameters of the model the system undergoes in time different phases of the structure. We conclude with a possible explanation of some empirical data on the connections between neurons. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Influence network linkages across implementation strategy conditions in a randomized controlled trial of two strategies for scaling up evidence-based practices in public youth-serving systems.

PubMed

Palinkas, Lawrence A; Holloway, Ian W; Rice, Eric; Brown, C Hendricks; Valente, Thomas W; Chamberlain, Patricia

2013-11-14

Given the importance of influence networks in the implementation of evidence-based practices and interventions, it is unclear whether such networks continue to operate as sources of information and advice when they are segmented and disrupted by randomization to different implementation strategy conditions. The present study examines the linkages across implementation strategy conditions of social influence networks of leaders of youth-serving systems in 12 California counties participating in a randomized controlled trial of community development teams (CDTs) to scale up use of an evidence-based practice. Semi-structured interviews were conducted with 38 directors, assistant directors, and program managers of county probation, mental health, and child welfare departments. A web-based survey collected additional quantitative data on information and advice networks of study participants. A mixed-methods approach to data analysis was used to create a sociometric data set (n = 176) to examine linkages between treatment and standard conditions. Of those network members who were affiliated with a county (n = 137), only 6 (4.4%) were directly connected to a member of the opposite implementation strategy condition; 19 (13.9%) were connected by two steps or fewer to a member of the opposite implementation strategy condition; 64 (46.7%) were connected by three or fewer steps to a member of the opposite implementation strategy condition. Most of the indirect steps between individuals who were in different implementation strategy conditions were connections involving a third non-county organizational entity that had an important role in the trial in keeping the implementation strategy conditions separate. When these entities were excluded, the CDT network exhibited fewer components and significantly higher betweenness centralization than did the standard condition network. Although the integrity of the RCT in this instance was not compromised by study participant influence networks, RCT designs should consider how influence networks may extend beyond boundaries established by the randomization process in implementation studies. NCT00880126.

Greedy Gossip With Eavesdropping

NASA Astrophysics Data System (ADS)

Ustebay, Deniz; Oreshkin, Boris N.; Coates, Mark J.; Rabbat, Michael G.

2010-07-01

This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average consensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and decentralized, making them attractive for wireless network applications. In general, gossip algorithms are robust to unreliable wireless conditions and time varying network topologies. In this paper we introduce GGE and demonstrate that greedy updates lead to rapid convergence. We do not require nodes to have any location information. Instead, greedy updates are made possible by exploiting the broadcast nature of wireless communications. During the operation of GGE, when a node decides to gossip, instead of choosing one of its neighbors at random, it makes a greedy selection, choosing the node which has the value most different from its own. In order to make this selection, nodes need to know their neighbors' values. Therefore, we assume that all transmissions are wireless broadcasts and nodes keep track of their neighbors' values by eavesdropping on their communications. We show that the convergence of GGE is guaranteed for connected network topologies. We also study the rates of convergence and illustrate, through theoretical bounds and numerical simulations, that GGE consistently outperforms randomized gossip and performs comparably to geographic gossip on moderate-sized random geometric graph topologies.

An Ancient Gene Network Is Co-opted for Teeth on Old and New Jaws

PubMed Central

Fraser, Gareth J; Hulsey, C. Darrin; Bloomquist, Ryan F; Uyesugi, Kristine; Manley, Nancy R; Streelman, J. Todd

2009-01-01

Vertebrate dentitions originated in the posterior pharynx of jawless fishes more than half a billion years ago. As gnathostomes (jawed vertebrates) evolved, teeth developed on oral jaws and helped to establish the dominance of this lineage on land and in the sea. The advent of oral jaws was facilitated, in part, by absence of hox gene expression in the first, most anterior, pharyngeal arch. Much later in evolutionary time, teleost fishes evolved a novel toothed jaw in the pharynx, the location of the first vertebrate teeth. To examine the evolutionary modularity of dentitions, we asked whether oral and pharyngeal teeth develop using common or independent gene regulatory pathways. First, we showed that tooth number is correlated on oral and pharyngeal jaws across species of cichlid fishes from Lake Malawi (East Africa), suggestive of common regulatory mechanisms for tooth initiation. Surprisingly, we found that cichlid pharyngeal dentitions develop in a region of dense hox gene expression. Thus, regulation of tooth number is conserved, despite distinct developmental environments of oral and pharyngeal jaws; pharyngeal jaws occupy hox-positive, endodermal sites, and oral jaws develop in hox-negative regions with ectodermal cell contributions. Next, we studied the expression of a dental gene network for tooth initiation, most genes of which are similarly deployed across the two disparate jaw sites. This collection of genes includes members of the ectodysplasin pathway, eda and edar, expressed identically during the patterning of oral and pharyngeal teeth. Taken together, these data suggest that pharyngeal teeth of jawless vertebrates utilized an ancient gene network before the origin of oral jaws, oral teeth, and ectodermal appendages. The first vertebrate dentition likely appeared in a hox-positive, endodermal environment and expressed a genetic program including ectodysplasin pathway genes. This ancient regulatory circuit was co-opted and modified for teeth in oral jaws of the first jawed vertebrate, and subsequently deployed as jaws enveloped teeth on novel pharyngeal jaws. Our data highlight an amazing modularity of jaws and teeth as they coevolved during the history of vertebrates. We exploit this diversity to infer a core dental gene network, common to the first tooth and all of its descendants. PMID:19215146

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

PubMed Central

2016-01-01

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

Statistics of premixed flame cells

NASA Technical Reports Server (NTRS)

Noever, David A.

1991-01-01

The statistics of random cellular patterns in premixed flames are analyzed. Agreement is found with a variety of topological relations previously found for other networks, namely, Lewis's law and Aboav's law. Despite the diverse underlying physics, flame cells are shown to share a broad class of geometric properties with other random networks-metal grains, soap foams, bioconvection, and Langmuir monolayers.

A mixing evolution model for bidirectional microblog user networks

NASA Astrophysics Data System (ADS)

Yuan, Wei-Guo; Liu, Yun

2015-08-01

Microblogs have been widely used as a new form of online social networking. Based on the user profile data collected from Sina Weibo, we find that the number of microblog user bidirectional friends approximately corresponds with the lognormal distribution. We then build two microblog user networks with real bidirectional relationships, both of which have not only small-world and scale-free but also some special properties, such as double power-law degree distribution, disassortative network, hierarchical and rich-club structure. Moreover, by detecting the community structures of the two real networks, we find both of their community scales follow an exponential distribution. Based on the empirical analysis, we present a novel evolution network model with mixed connection rules, including lognormal fitness preferential and random attachment, nearest neighbor interconnected in the same community, and global random associations in different communities. The simulation results show that our model is consistent with real network in many topology features.

Noise enhances information transfer in hierarchical networks.

PubMed

Czaplicka, Agnieszka; Holyst, Janusz A; Sloot, Peter M A

2013-01-01

We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor.

Noise enhances information transfer in hierarchical networks

PubMed Central

Czaplicka, Agnieszka; Holyst, Janusz A.; Sloot, Peter M. A.

2013-01-01

We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor. PMID:23390574

Failure tolerance of spike phase synchronization in coupled neural networks

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2011-09-01

Neuronal synchronization plays an important role in the various functionality of nervous system such as binding, cognition, information processing, and computation. In this paper, we investigated how random and intentional failures in the nodes of a network influence its phase synchronization properties. We considered both artificially constructed networks using models such as preferential attachment, Watts-Strogatz, and Erdős-Rényi as well as a number of real neuronal networks. The failure strategy was either random or intentional based on properties of the nodes such as degree, clustering coefficient, betweenness centrality, and vulnerability. Hindmarsh-Rose model was considered as the mathematical model for the individual neurons, and the phase synchronization of the spike trains was monitored as a function of the percentage/number of removed nodes. The numerical simulations were supplemented by considering coupled non-identical Kuramoto oscillators. Failures based on the clustering coefficient, i.e., removing the nodes with high values of the clustering coefficient, had the least effect on the spike synchrony in all of the networks. This was followed by errors where the nodes were removed randomly. However, the behavior of the other three attack strategies was not uniform across the networks, and different strategies were the most influential in different network structure.

A Peer-Educator Network HIV Prevention Intervention Among Injection Drug Users: Results of a Randomized Controlled Trial in St. Petersburg, Russia

PubMed Central

Latkin, Carl A.; Kukhareva, Polina V.; Malov, Sergey V.; Batluk, Julia V.; Shaboltas, Alla V.; Skochilov, Roman V.; Sokolov, Nicolay V.; Verevochkin, Sergei V.; Hudgens, Michael G.; Kozlov, Andrei P.

2014-01-01

We evaluated the efficacy of a peer-educator network intervention as a strategy to reduce HIV acquisition among injection drug users (IDUs) and their drug and/or sexual networks. A randomized controlled trial was conducted in St. Petersburg, Russia among IDU index participants and their risk network participants. Network units were randomized to the control or experimental intervention. Only the experimental index participants received training sessions to communicate risk reduction techniques to their network members. Analysis includes 76 index and 84 network participants who were HIV uninfected. The main outcome measure was HIV sero-conversion. The incidence rates in the control and experimental groups were 19.57 (95 % CI 10.74–35.65) and 7.76 (95 % CI 3.51–17.19) cases per 100 p/y, respectively. The IRR was 0.41 (95 % CI 0.15–1.08) without a statistically significant difference between the two groups (log rank test statistic X2 = 2.73, permutation p value = 0.16). Retention rate was 67 % with a third of the loss due to incarceration or death. The results show a promising trend that this strategy would be successful in reducing the acquisition of HIV among IDUs. PMID:23881187

Analysis and Reduction of Complex Networks Under Uncertainty.

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

Ghanem, Roger G

2014-07-31

This effort was a collaboration with Youssef Marzouk of MIT, Omar Knio of Duke University (at the time at Johns Hopkins University) and Habib Najm of Sandia National Laboratories. The objective of this effort was to develop the mathematical and algorithmic capacity to analyze complex networks under uncertainty. Of interest were chemical reaction networks and smart grid networks. The statements of work for USC focused on the development of stochastic reduced models for uncertain networks. The USC team was led by Professor Roger Ghanem and consisted of one graduate student and a postdoc. The contributions completed by the USC teammore » consisted of 1) methodology and algorithms to address the eigenvalue problem, a problem of significance in the stability of networks under stochastic perturbations, 2) methodology and algorithms to characterize probability measures on graph structures with random flows. This is an important problem in characterizing random demand (encountered in smart grid) and random degradation (encountered in infrastructure systems), as well as modeling errors in Markov Chains (with ubiquitous relevance !). 3) methodology and algorithms for treating inequalities in uncertain systems. This is an important problem in the context of models for material failure and network flows under uncertainty where conditions of failure or flow are described in the form of inequalities between the state variables.« less

Random and Directed Walk-Based Top-k Queries in Wireless Sensor Networks

PubMed Central

Fu, Jun-Song; Liu, Yun

2015-01-01

In wireless sensor networks, filter-based top-k query approaches are the state-of-the-art solutions and have been extensively researched in the literature, however, they are very sensitive to the network parameters, including the size of the network, dynamics of the sensors’ readings and declines in the overall range of all the readings. In this work, a random walk-based top-k query approach called RWTQ and a directed walk-based top-k query approach called DWTQ are proposed. At the beginning of a top-k query, one or several tokens are sent to the specific node(s) in the network by the base station. Then, each token walks in the network independently to record and process the readings in a random or directed way. A strategy of choosing the “right” way in DWTQ is carefully designed for the token(s) to arrive at the high-value regions as soon as possible. When designing the walking strategy for DWTQ, the spatial correlations of the readings are also considered. Theoretical analysis and simulation results indicate that RWTQ and DWTQ both are very robust against these parameters discussed previously. In addition, DWTQ outperforms TAG, FILA and EXTOK in transmission cost, energy consumption and network lifetime. PMID:26016914

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.

Spike phase synchronization in multiplex cortical neural networks

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2017-01-01

In this paper we study synchronizability of two multiplex cortical networks: whole-cortex of hermaphrodite C. elegans and posterior cortex in male C. elegans. These networks are composed of two connection layers: network of chemical synapses and the one formed by gap junctions. This work studies the contribution of each layer on the phase synchronization of non-identical spiking Hindmarsh-Rose neurons. The network of male C. elegans shows higher phase synchronization than its randomized version, while it is not the case for hermaphrodite type. The random networks in each layer are constructed such that the nodes have the same degree as the original network, thus providing an unbiased comparison. In male C. elegans, although the gap junction network is sparser than the chemical network, it shows higher contribution in the synchronization phenomenon. This is not the case in hermaphrodite type, which is mainly due to significant less density of gap junction layer (0.013) as compared to chemical layer (0.028). Also, the gap junction network in this type has stronger community structure than the chemical network, and this is another driving factor for its weaker synchronizability.

Investigating Cell Criticality

NASA Astrophysics Data System (ADS)

Serra, R.; Villani, M.; Damiani, C.; Graudenzi, A.; Ingrami, P.; Colacci, A.

Random Boolean networks provide a way to give a precise meaning to the notion that living beings are in a critical state. Some phenomena which are observed in real biological systems (distribution of "avalanches" in gene knock-out experiments) can be modeled using random Boolean networks, and the results can be analytically proven to depend upon the Derrida parameter, which also determines whether the network is critical. By comparing observed and simulated data one can then draw inferences about the criticality of biological cells, although with some care because of the limited number of experimental observations. The relationship between the criticality of a single network and that of a set of interacting networks, which simulate a tissue or a bacterial colony, is also analyzed by computer simulations.

Update on exotic ash collection for hybrid breeding and survey for EAB-resistance in native North American species

Treesearch

Mary E. Mason; Daniel A. Herms; David W. Carey; Kathleen S. Knight; Nurul I. Faridi; Jennifer. Koch

2011-01-01

Contrary to the high levels of devastation observed on North American ash species infested with emerald ash borer (EAB) (Agrilus planipennis Fairmaire), reports from Asia indicate that EAB-induced destruction of Asian ash species is limited to stressed trees. This indicates that Asian ash species have co-evolved resistance, or at least a high degree...

Analysis of a SNP Linked to Lactase Persistence: An Exercise for Teaching Molecular Biology Techniques to Undergraduates

ERIC Educational Resources Information Center

Schultheis, Patrick J.; Bowling, Bethany V.

2011-01-01

Recent experimental evidence indicates that the ability of adults to tolerate milk, cheese, and other lactose-containing dairy products is an autosomal dominant trait that co-evolved with dairy farming in Central Europe about 7,500 years ago. Among persons of European descent, this trait is strongly associated with a C to T substitution at a…

Impact of self-healing capability on network robustness

NASA Astrophysics Data System (ADS)

Shang, Yilun

2015-04-01

A wide spectrum of real-life systems ranging from neurons to botnets display spontaneous recovery ability. Using the generating function formalism applied to static uncorrelated random networks with arbitrary degree distributions, the microscopic mechanism underlying the depreciation-recovery process is characterized and the effect of varying self-healing capability on network robustness is revealed. It is found that the self-healing capability of nodes has a profound impact on the phase transition in the emergence of percolating clusters, and that salient difference exists in upholding network integrity under random failures and intentional attacks. The results provide a theoretical framework for quantitatively understanding the self-healing phenomenon in varied complex systems.

Impact of self-healing capability on network robustness.

PubMed

Shang, Yilun

2015-04-01

A wide spectrum of real-life systems ranging from neurons to botnets display spontaneous recovery ability. Using the generating function formalism applied to static uncorrelated random networks with arbitrary degree distributions, the microscopic mechanism underlying the depreciation-recovery process is characterized and the effect of varying self-healing capability on network robustness is revealed. It is found that the self-healing capability of nodes has a profound impact on the phase transition in the emergence of percolating clusters, and that salient difference exists in upholding network integrity under random failures and intentional attacks. The results provide a theoretical framework for quantitatively understanding the self-healing phenomenon in varied complex systems.

An improved sampling method of complex network

NASA Astrophysics Data System (ADS)

Gao, Qi; Ding, Xintong; Pan, Feng; Li, Weixing

2014-12-01

Sampling subnet is an important topic of complex network research. Sampling methods influence the structure and characteristics of subnet. Random multiple snowball with Cohen (RMSC) process sampling which combines the advantages of random sampling and snowball sampling is proposed in this paper. It has the ability to explore global information and discover the local structure at the same time. The experiments indicate that this novel sampling method could keep the similarity between sampling subnet and original network on degree distribution, connectivity rate and average shortest path. This method is applicable to the situation where the prior knowledge about degree distribution of original network is not sufficient.

Pseudo-random dynamic address configuration (PRDAC) algorithm for mobile ad hoc networks

NASA Astrophysics Data System (ADS)

Wu, Shaochuan; Tan, Xuezhi

2007-11-01

By analyzing all kinds of address configuration algorithms, this paper provides a new pseudo-random dynamic address configuration (PRDAC) algorithm for mobile ad hoc networks. Based on PRDAC, the first node that initials this network randomly chooses a nonlinear shift register that can generates an m-sequence. When another node joins this network, the initial node will act as an IP address configuration sever to compute an IP address according to this nonlinear shift register, and then allocates this address and tell the generator polynomial of this shift register to this new node. By this means, when other node joins this network, any node that has obtained an IP address can act as a server to allocate address to this new node. PRDAC can also efficiently avoid IP conflicts and deal with network partition and merge as same as prophet address (PA) allocation and dynamic configuration and distribution protocol (DCDP). Furthermore, PRDAC has less algorithm complexity, less computational complexity and more sufficient assumption than PA. In addition, PRDAC radically avoids address conflicts and maximizes the utilization rate of IP addresses. Analysis and simulation results show that PRDAC has rapid convergence, low overhead and immune from topological structures.

Knowledge diffusion of dynamical network in terms of interaction frequency.

PubMed

Liu, Jian-Guo; Zhou, Qing; Guo, Qiang; Yang, Zhen-Hua; Xie, Fei; Han, Jing-Ti

2017-09-07

In this paper, we present a knowledge diffusion (SKD) model for dynamic networks by taking into account the interaction frequency which always used to measure the social closeness. A set of agents, which are initially interconnected to form a random network, either exchange knowledge with their neighbors or move toward a new location through an edge-rewiring procedure. The activity of knowledge exchange between agents is determined by a knowledge transfer rule that the target node would preferentially select one neighbor node to transfer knowledge with probability p according to their interaction frequency instead of the knowledge distance, otherwise, the target node would build a new link with its second-order neighbor preferentially or select one node in the system randomly with probability 1 - p. The simulation results show that, comparing with the Null model defined by the random selection mechanism and the traditional knowledge diffusion (TKD) model driven by knowledge distance, the knowledge would spread more fast based on SKD driven by interaction frequency. In particular, the network structure of SKD would evolve as an assortative one, which is a fundamental feature of social networks. This work would be helpful for deeply understanding the coevolution of the knowledge diffusion and network structure.

Random Visitor: Defense against Identity Attacks in P2P Networks

NASA Astrophysics Data System (ADS)

Gu, Jabeom; Nah, Jaehoon; Kwon, Hyeokchan; Jang, Jonsoo; Park, Sehyun

Various advantages of cooperative peer-to-peer networks are strongly counterbalanced by the open nature of a distributed, serverless network. In such networks, it is relatively easy for an attacker to launch various attacks such as misrouting, corrupting, or dropping messages as a result of a successful identifier forgery. The impact of an identifier forgery is particularly severe because the whole network can be compromised by attacks such as Sybil or Eclipse. In this paper, we present an identifier authentication mechanism called random visitor, which uses one or more randomly selected peers as delegates of identity proof. Our scheme uses identity-based cryptography and identity ownership proof mechanisms collectively to create multiple, cryptographically protected indirect bindings between two peers, instantly when needed, through the delegates. Because of these bindings, an attacker cannot achieve an identifier forgery related attack against interacting peers without breaking the bindings. Therefore, our mechanism limits the possibility of identifier forgery attacks efficiently by disabling an attacker's ability to break the binding. The design rationale and framework details are presented. A security analysis shows that our scheme is strong enough against identifier related attacks and that the strength increases if there are many peers (more than several thousand) in the network.

Impact of peer-led quality improvement networks on quality of inpatient mental health care: study protocol for a cluster randomized controlled trial.

PubMed

Aimola, Lina; Jasim, Sarah; Tripathi, Neeraj; Tucker, Sarah; Worrall, Adrian; Quirk, Alan; Crawford, Mike J

2016-09-21

Quality improvement networks are peer-led programmes in which members of the network assess the quality of care colleagues provide according to agreed standards of practice. These networks aim to help members identify areas of service provision that could be improved and share good practice. Despite the widespread use of peer-led quality improvement networks, there is very little information about their impact. We are conducting a cluster randomized controlled trial of a quality improvement network for low-secure mental health wards to examine the impact of membership on the process and outcomes of care over a 12 month period. Standalone low secure units in England and Wales that expressed an interest in joining the quality improvement network were recruited for the study from 2012 to 2014. Thirty-eight units were randomly allocated to either the active intervention (participation in the network n = 18) or a control arm (delayed participation in the network n = 20). Using a 5 % significance level and 90 % power, it was calculated that a sample size of 60 wards was required taking into account a 10 % drop out. A total of 75 wards were assessed at baseline and 8 wards dropped out the study before the data collection at follow up. Researchers masked to the allocation status of the units assessed all study outcomes at baseline and follow-up 12 months later. The primary outcome is the quality of the physical environment and facilities on the wards. The secondary outcomes are: safety of the ward, patient-rated satisfaction with care and mental well-being, staff burnout, training and supervision. Relative to control wards, it is hypothesized that the quality of the physical environment and facilities will be higher on wards in the active arm of the trial 12 months after randomization. To our knowledge, this is the first randomized evaluation of a peer-led quality improvement network that has examined the impact of participation on both patient-level and service-level outcomes. The study has the potential to help shape future efforts to improve the quality of inpatient care. Current Controlled Trials ISRCTN79614916 . Retrospectively registered 28 March 2014].

Coevolution between Hispaniolan crossbills and pine: does more time allow for greater phenotypic escalation at lower latitude?

PubMed

Parchman, Thomas L; Benkman, Craig W; Mezquida, Eduardo T

2007-09-01

Crossbills (Aves: Loxia) and several conifers have coevolved in predator-prey arms races over the last 10,000 years. However, the extent to which coevolutionary arms races have contributed to the adaptive radiation of crossbills or to any other adaptive radiation is largely unknown. Here we extend our previous studies of geographically structured coevolution by considering a crossbill-conifer interaction that has persisted for a much longer time period and involves a conifer with more variable annual seed production. We examined geographic variation in the cone and seed traits of two sister species of pines, Pinus occidentalis and P. cubensis, on the islands of Hispaniola and Cuba, respectively. We also compared the Hispaniolan crossbill (Loxia megaplaga) to its sister taxa the North American white-winged crossbill (Loxia leucoptera leucoptera). The Hispaniolan crossbill is endemic to Hispaniola whereas Cuba lacks crossbills. In addition and in contrast to previous studies, the variation in selection experienced by these pines due to crossbills is not confounded by the occurrence of selection by tree squirrels (Tamiasciurus and Sciurus). As predicted if P. occidentalis has evolved defenses in response to selection exerted by crossbills, cones of P. occidentalis have scales that are 53% thicker than those of P. cubensis. Cones of P. occidentalis, but not P. cubensis, also have well-developed spines, a known defense against vertebrate seed predators. Consistent with patterns of divergence seen in crossbills coevolving locally with other conifers, the Hispaniolan crossbill has evolved a bill that is 25% deeper than the white-winged crossbill. Together with phylogenetic analyses, our results suggest that predator-prey coevolution between Hispaniolan crossbills and P. occidentalis over approximately 600,000 years has caused substantial morphological evolution in both the crossbill and pine. This also indicates that cone crop fluctuations do not prevent crossbills and conifers from coevolving. Furthermore, because the traits at the phenotypic interface of the interaction apparently remain the same over at least several hundred thousand years, divergence as a result of coevolution is greater at lower latitude where crossbill-conifer interactions have been less interrupted by Pleistocene events.

Sequentially switching cell assemblies in random inhibitory networks of spiking neurons in the striatum.

PubMed

Ponzi, Adam; Wickens, Jeff

2010-04-28

The striatum is composed of GABAergic medium spiny neurons with inhibitory collaterals forming a sparse random asymmetric network and receiving an excitatory glutamatergic cortical projection. Because the inhibitory collaterals are sparse and weak, their role in striatal network dynamics is puzzling. However, here we show by simulation of a striatal inhibitory network model composed of spiking neurons that cells form assemblies that fire in sequential coherent episodes and display complex identity-temporal spiking patterns even when cortical excitation is simply constant or fluctuating noisily. Strongly correlated large-scale firing rate fluctuations on slow behaviorally relevant timescales of hundreds of milliseconds are shown by members of the same assembly whereas members of different assemblies show strong negative correlation, and we show how randomly connected spiking networks can generate this activity. Cells display highly irregular spiking with high coefficients of variation, broadly distributed low firing rates, and interspike interval distributions that are consistent with exponentially tailed power laws. Although firing rates vary coherently on slow timescales, precise spiking synchronization is absent in general. Our model only requires the minimal but striatally realistic assumptions of sparse to intermediate random connectivity, weak inhibitory synapses, and sufficient cortical excitation so that some cells are depolarized above the firing threshold during up states. Our results are in good qualitative agreement with experimental studies, consistent with recently determined striatal anatomy and physiology, and support a new view of endogenously generated metastable state switching dynamics of the striatal network underlying its information processing operations.

Navigability of Random Geometric Graphs in the Universe and Other Spacetimes.

PubMed

Cunningham, William; Zuev, Konstantin; Krioukov, Dmitri

2017-08-18

Random geometric graphs in hyperbolic spaces explain many common structural and dynamical properties of real networks, yet they fail to predict the correct values of the exponents of power-law degree distributions observed in real networks. In that respect, random geometric graphs in asymptotically de Sitter spacetimes, such as the Lorentzian spacetime of our accelerating universe, are more attractive as their predictions are more consistent with observations in real networks. Yet another important property of hyperbolic graphs is their navigability, and it remains unclear if de Sitter graphs are as navigable as hyperbolic ones. Here we study the navigability of random geometric graphs in three Lorentzian manifolds corresponding to universes filled only with dark energy (de Sitter spacetime), only with matter, and with a mixture of dark energy and matter. We find these graphs are navigable only in the manifolds with dark energy. This result implies that, in terms of navigability, random geometric graphs in asymptotically de Sitter spacetimes are as good as random hyperbolic graphs. It also establishes a connection between the presence of dark energy and navigability of the discretized causal structure of spacetime, which provides a basis for a different approach to the dark energy problem in cosmology.

Composing Music with Complex Networks

NASA Astrophysics Data System (ADS)

Liu, Xiaofan; Tse, Chi K.; Small, Michael

In this paper we study the network structure in music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurrences. We analyze sample compositions from Bach, Mozart, Chopin, as well as other types of music including Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. Power-law exponents of degree distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be created by using a biased random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. The newly created music from complex networks will be played in the presentation.

Emergence of small-world structure in networks of spiking neurons through STDP plasticity.

PubMed

Basalyga, Gleb; Gleiser, Pablo M; Wennekers, Thomas

2011-01-01

In this work, we use a complex network approach to investigate how a neural network structure changes under synaptic plasticity. In particular, we consider a network of conductance-based, single-compartment integrate-and-fire excitatory and inhibitory neurons. Initially the neurons are connected randomly with uniformly distributed synaptic weights. The weights of excitatory connections can be strengthened or weakened during spiking activity by the mechanism known as spike-timing-dependent plasticity (STDP). We extract a binary directed connection matrix by thresholding the weights of the excitatory connections at every simulation step and calculate its major topological characteristics such as the network clustering coefficient, characteristic path length and small-world index. We numerically demonstrate that, under certain conditions, a nontrivial small-world structure can emerge from a random initial network subject to STDP learning.

Investigation of a protein complex network

NASA Astrophysics Data System (ADS)

Mashaghi, A. R.; Ramezanpour, A.; Karimipour, V.

2004-09-01

The budding yeast Saccharomyces cerevisiae is the first eukaryote whose genome has been completely sequenced. It is also the first eukaryotic cell whose proteome (the set of all proteins) and interactome (the network of all mutual interactions between proteins) has been analyzed. In this paper we study the structure of the yeast protein complex network in which weighted edges between complexes represent the number of shared proteins. It is found that the network of protein complexes is a small world network with scale free behavior for many of its distributions. However we find that there are no strong correlations between the weights and degrees of neighboring complexes. To reveal non-random features of the network we also compare it with a null model in which the complexes randomly select their proteins. Finally we propose a simple evolutionary model based on duplication and divergence of proteins.

Using Exponential Random Graph Models to Analyze the Character of Peer Relationship Networks and Their Effects on the Subjective Well-being of Adolescents.

PubMed

Jiao, Can; Wang, Ting; Liu, Jianxin; Wu, Huanjie; Cui, Fang; Peng, Xiaozhe

2017-01-01

The influences of peer relationships on adolescent subjective well-being were investigated within the framework of social network analysis, using exponential random graph models as a methodological tool. The participants in the study were 1,279 students (678 boys and 601 girls) from nine junior middle schools in Shenzhen, China. The initial stage of the research used a peer nomination questionnaire and a subjective well-being scale (used in previous studies) to collect data on the peer relationship networks and the subjective well-being of the students. Exponential random graph models were then used to explore the relationships between students with the aim of clarifying the character of the peer relationship networks and the influence of peer relationships on subjective well being. The results showed that all the adolescent peer relationship networks in our investigation had positive reciprocal effects, positive transitivity effects and negative expansiveness effects. However, none of the relationship networks had obvious receiver effects or leaders. The adolescents in partial peer relationship networks presented similar levels of subjective well-being on three dimensions (satisfaction with life, positive affects and negative affects) though not all network friends presented these similarities. The study shows that peer networks can affect an individual's subjective well-being. However, whether similarities among adolescents are the result of social influences or social choices needs further exploration, including longitudinal studies that investigate the potential processes of subjective well-being similarities among adolescents.

Using Exponential Random Graph Models to Analyze the Character of Peer Relationship Networks and Their Effects on the Subjective Well-being of Adolescents

PubMed Central

Jiao, Can; Wang, Ting; Liu, Jianxin; Wu, Huanjie; Cui, Fang; Peng, Xiaozhe

2017-01-01

The influences of peer relationships on adolescent subjective well-being were investigated within the framework of social network analysis, using exponential random graph models as a methodological tool. The participants in the study were 1,279 students (678 boys and 601 girls) from nine junior middle schools in Shenzhen, China. The initial stage of the research used a peer nomination questionnaire and a subjective well-being scale (used in previous studies) to collect data on the peer relationship networks and the subjective well-being of the students. Exponential random graph models were then used to explore the relationships between students with the aim of clarifying the character of the peer relationship networks and the influence of peer relationships on subjective well being. The results showed that all the adolescent peer relationship networks in our investigation had positive reciprocal effects, positive transitivity effects and negative expansiveness effects. However, none of the relationship networks had obvious receiver effects or leaders. The adolescents in partial peer relationship networks presented similar levels of subjective well-being on three dimensions (satisfaction with life, positive affects and negative affects) though not all network friends presented these similarities. The study shows that peer networks can affect an individual’s subjective well-being. However, whether similarities among adolescents are the result of social influences or social choices needs further exploration, including longitudinal studies that investigate the potential processes of subjective well-being similarities among adolescents. PMID:28450845

Random domain name and address mutation (RDAM) for thwarting reconnaissance attacks

PubMed Central

Chen, Xi; Zhu, Yuefei

2017-01-01

Network address shuffling is a novel moving target defense (MTD) that invalidates the address information collected by the attacker by dynamically changing or remapping the host’s network addresses. However, most network address shuffling methods are limited by the limited address space and rely on the host’s static domain name to map to its dynamic address; therefore these methods cannot effectively defend against random scanning attacks, and cannot defend against an attacker who knows the target’s domain name. In this paper, we propose a network defense method based on random domain name and address mutation (RDAM), which increases the scanning space of the attacker through a dynamic domain name method and reduces the probability that a host will be hit by an attacker scanning IP addresses using the domain name system (DNS) query list and the time window methods. Theoretical analysis and experimental results show that RDAM can defend against scanning attacks and worm propagation more effectively than general network address shuffling methods, while introducing an acceptable operational overhead. PMID:28489910

Complex growing networks with intrinsic vertex fitness

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

Bedogne, C.; Rodgers, G. J.

2006-10-15

One of the major questions in complex network research is to identify the range of mechanisms by which a complex network can self organize into a scale-free state. In this paper we investigate the interplay between a fitness linking mechanism and both random and preferential attachment. In our models, each vertex is assigned a fitness x, drawn from a probability distribution {rho}(x). In Model A, at each time step a vertex is added and joined to an existing vertex, selected at random, with probability p and an edge is introduced between vertices with fitnesses x and y, with a ratemore » f(x,y), with probability 1-p. Model B differs from Model A in that, with probability p, edges are added with preferential attachment rather than randomly. The analysis of Model A shows that, for every fixed fitness x, the network's degree distribution decays exponentially. In Model B we recover instead a power-law degree distribution whose exponent depends only on p, and we show how this result can be generalized. The properties of a number of particular networks are examined.« less

Mean-field equations for neuronal networks with arbitrary degree distributions.

PubMed

Nykamp, Duane Q; Friedman, Daniel; Shaker, Sammy; Shinn, Maxwell; Vella, Michael; Compte, Albert; Roxin, Alex

2017-04-01

The emergent dynamics in networks of recurrently coupled spiking neurons depends on the interplay between single-cell dynamics and network topology. Most theoretical studies on network dynamics have assumed simple topologies, such as connections that are made randomly and independently with a fixed probability (Erdös-Rényi network) (ER) or all-to-all connected networks. However, recent findings from slice experiments suggest that the actual patterns of connectivity between cortical neurons are more structured than in the ER random network. Here we explore how introducing additional higher-order statistical structure into the connectivity can affect the dynamics in neuronal networks. Specifically, we consider networks in which the number of presynaptic and postsynaptic contacts for each neuron, the degrees, are drawn from a joint degree distribution. We derive mean-field equations for a single population of homogeneous neurons and for a network of excitatory and inhibitory neurons, where the neurons can have arbitrary degree distributions. Through analysis of the mean-field equations and simulation of networks of integrate-and-fire neurons, we show that such networks have potentially much richer dynamics than an equivalent ER network. Finally, we relate the degree distributions to so-called cortical motifs.

Mean-field equations for neuronal networks with arbitrary degree distributions

NASA Astrophysics Data System (ADS)

Nykamp, Duane Q.; Friedman, Daniel; Shaker, Sammy; Shinn, Maxwell; Vella, Michael; Compte, Albert; Roxin, Alex

2017-04-01

The emergent dynamics in networks of recurrently coupled spiking neurons depends on the interplay between single-cell dynamics and network topology. Most theoretical studies on network dynamics have assumed simple topologies, such as connections that are made randomly and independently with a fixed probability (Erdös-Rényi network) (ER) or all-to-all connected networks. However, recent findings from slice experiments suggest that the actual patterns of connectivity between cortical neurons are more structured than in the ER random network. Here we explore how introducing additional higher-order statistical structure into the connectivity can affect the dynamics in neuronal networks. Specifically, we consider networks in which the number of presynaptic and postsynaptic contacts for each neuron, the degrees, are drawn from a joint degree distribution. We derive mean-field equations for a single population of homogeneous neurons and for a network of excitatory and inhibitory neurons, where the neurons can have arbitrary degree distributions. Through analysis of the mean-field equations and simulation of networks of integrate-and-fire neurons, we show that such networks have potentially much richer dynamics than an equivalent ER network. Finally, we relate the degree distributions to so-called cortical motifs.

Small Modifications to Network Topology Can Induce Stochastic Bistable Spiking Dynamics in a Balanced Cortical Model

PubMed Central

McDonnell, Mark D.; Ward, Lawrence M.

2014-01-01

Abstract Directed random graph models frequently are used successfully in modeling the population dynamics of networks of cortical neurons connected by chemical synapses. Experimental results consistently reveal that neuronal network topology is complex, however, in the sense that it differs statistically from a random network, and differs for classes of neurons that are physiologically different. This suggests that complex network models whose subnetworks have distinct topological structure may be a useful, and more biologically realistic, alternative to random networks. Here we demonstrate that the balanced excitation and inhibition frequently observed in small cortical regions can transiently disappear in otherwise standard neuronal-scale models of fluctuation-driven dynamics, solely because the random network topology was replaced by a complex clustered one, whilst not changing the in-degree of any neurons. In this network, a small subset of cells whose inhibition comes only from outside their local cluster are the cause of bistable population dynamics, where different clusters of these cells irregularly switch back and forth from a sparsely firing state to a highly active state. Transitions to the highly active state occur when a cluster of these cells spikes sufficiently often to cause strong unbalanced positive feedback to each other. Transitions back to the sparsely firing state rely on occasional large fluctuations in the amount of non-local inhibition received. Neurons in the model are homogeneous in their intrinsic dynamics and in-degrees, but differ in the abundance of various directed feedback motifs in which they participate. Our findings suggest that (i) models and simulations should take into account complex structure that varies for neuron and synapse classes; (ii) differences in the dynamics of neurons with similar intrinsic properties may be caused by their membership in distinctive local networks; (iii) it is important to identify neurons that share physiological properties and location, but differ in their connectivity. PMID:24743633

Model Of Neural Network With Creative Dynamics

NASA Technical Reports Server (NTRS)

Zak, Michail; Barhen, Jacob

1993-01-01

Paper presents analysis of mathematical model of one-neuron/one-synapse neural network featuring coupled activation and learning dynamics and parametrical periodic excitation. Demonstrates self-programming, partly random behavior of suitable designed neural network; believed to be related to spontaneity and creativity of biological neural networks.

Robustness of spatial micronetworks

NASA Astrophysics Data System (ADS)

McAndrew, Thomas C.; Danforth, Christopher M.; Bagrow, James P.

2015-04-01

Power lines, roadways, pipelines, and other physical infrastructure are critical to modern society. These structures may be viewed as spatial networks where geographic distances play a role in the functionality and construction cost of links. Traditionally, studies of network robustness have primarily considered the connectedness of large, random networks. Yet for spatial infrastructure, physical distances must also play a role in network robustness. Understanding the robustness of small spatial networks is particularly important with the increasing interest in microgrids, i.e., small-area distributed power grids that are well suited to using renewable energy resources. We study the random failures of links in small networks where functionality depends on both spatial distance and topological connectedness. By introducing a percolation model where the failure of each link is proportional to its spatial length, we find that when failures depend on spatial distances, networks are more fragile than expected. Accounting for spatial effects in both construction and robustness is important for designing efficient microgrids and other network infrastructure.

Non-criticality of interaction network over system's crises: A percolation analysis.

PubMed

Shirazi, Amir Hossein; Saberi, Abbas Ali; Hosseiny, Ali; Amirzadeh, Ehsan; Toranj Simin, Pourya

2017-11-20

Extraction of interaction networks from multi-variate time-series is one of the topics of broad interest in complex systems. Although this method has a wide range of applications, most of the previous analyses have focused on the pairwise relations. Here we establish the potential of such a method to elicit aggregated behavior of the system by making a connection with the concepts from percolation theory. We study the dynamical interaction networks of a financial market extracted from the correlation network of indices, and build a weighted network. In correspondence with the percolation model, we find that away from financial crises the interaction network behaves like a critical random network of Erdős-Rényi, while close to a financial crisis, our model deviates from the critical random network and behaves differently at different size scales. We perform further analysis to clarify that our observation is not a simple consequence of the growth in correlations over the crises.

Unprotected sex of homeless youth: results from a multilevel dyadic analysis of individual, social network, and relationship factors.

PubMed

Kennedy, David P; Tucker, Joan S; Green, Harold D; Golinelli, Daniela; Ewing, Brett

2012-10-01

Homeless youth have elevated risk of HIV through sexual behavior. This project investigates the multiple levels of influence on unprotected sex among homeless youth, including social network, individual, and partner level influences. Findings are based on analyses of an exploratory, semi-structured interview (n = 40) and a structured personal network interview (n = 240) with randomly selected homeless youth in Los Angeles. Previous social network studies of risky sex by homeless youth have collected limited social network data from non-random samples and have not distinguished sex partner influences from other network influences. The present analyses have identified significant associations with unprotected sex at multiple levels, including individual, partner, and, to a lesser extent, the social network. Analyses also distinguished between youth who did or did not want to use condoms when they had unprotected sex. Implications for social network based HIV risk interventions with homeless youth are discussed.

Unprotected Sex of Homeless Youth: Results from a Multilevel Analysis of Individual, Social Network, and Relationship Factors

PubMed Central

Kennedy, David P.; Tucker, Joan S.; Green, Harold D.; Golinelli, Daniela; Ewing, Brett

2012-01-01

Homeless youth have elevated risk of HIV through sexual behavior. This project investigates the multiple levels of influence on unprotected sex among homeless youth, including social network, individual, and partner level influences. Findings are based on analyses of an exploratory, semi-structured interview (n=40) and a structured personal network interview (n=240) with randomly selected homeless youth in Los Angeles. Previous social network studies of risky sex by homeless youth have collected limited social network data from non-random samples and have not distinguished sex partner influences from other network influences. The present analyses have identified significant associations with unprotected sex at multiple levels, including individual, partner, and, to a lesser extent, the social network. Analyses also distinguished between youth who wished they used condoms after having unprotected sex and youth who did not regret having unprotected sex. Implications for social network based HIV risk interventions with homeless youth are discussed. PMID:22610421

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

Engineering Online and In-person Social Networks for Physical Activity: A Randomized Trial

PubMed Central

Rovniak, Liza S.; Kong, Lan; Hovell, Melbourne F.; Ding, Ding; Sallis, James F.; Ray, Chester A.; Kraschnewski, Jennifer L.; Matthews, Stephen A.; Kiser, Elizabeth; Chinchilli, Vernon M.; George, Daniel R.; Sciamanna, Christopher N.

2016-01-01

Background Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. Purpose To conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively-measured outcomes. Methods Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3% male, 83.4% overweight/obese) were randomized to 1 of 3 groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking, and prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Results Participants increased their MVPA by 21.0 mins/week, 95% CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Conclusions Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. Trial Registration Number NCT01142804 PMID:27405724

A Mixed-Methods Randomized Controlled Trial of Financial Incentives and Peer Networks to Promote Walking among Older Adults

ERIC Educational Resources Information Center

Kullgren, Jeffrey T.; Harkins, Kristin A.; Bellamy, Scarlett L.; Gonzales, Amy; Tao, Yuanyuan; Zhu, Jingsan; Volpp, Kevin G.; Asch, David A.; Heisler, Michele; Karlawish, Jason

2014-01-01

Background: Financial incentives and peer networks could be delivered through eHealth technologies to encourage older adults to walk more. Methods: We conducted a 24-week randomized trial in which 92 older adults with a computer and Internet access received a pedometer, daily walking goals, and weekly feedback on goal achievement. Participants…

The robustness of multiplex networks under layer node-based attack

PubMed Central

Zhao, Da-wei; Wang, Lian-hai; Zhi, Yong-feng; Zhang, Jun; Wang, Zhen

2016-01-01

From transportation networks to complex infrastructures, and to social and economic networks, a large variety of systems can be described in terms of multiplex networks formed by a set of nodes interacting through different network layers. Network robustness, as one of the most successful application areas of complex networks, has attracted great interest in a myriad of research realms. In this regard, how multiplex networks respond to potential attack is still an open issue. Here we study the robustness of multiplex networks under layer node-based random or targeted attack, which means that nodes just suffer attacks in a given layer yet no additional influence to their connections beyond this layer. A theoretical analysis framework is proposed to calculate the critical threshold and the size of giant component of multiplex networks when nodes are removed randomly or intentionally. Via numerous simulations, it is unveiled that the theoretical method can accurately predict the threshold and the size of giant component, irrespective of attack strategies. Moreover, we also compare the robustness of multiplex networks under multiplex node-based attack and layer node-based attack, and find that layer node-based attack makes multiplex networks more vulnerable, regardless of average degree and underlying topology. PMID:27075870

The robustness of multiplex networks under layer node-based attack.

PubMed

Zhao, Da-wei; Wang, Lian-hai; Zhi, Yong-feng; Zhang, Jun; Wang, Zhen

2016-04-14

From transportation networks to complex infrastructures, and to social and economic networks, a large variety of systems can be described in terms of multiplex networks formed by a set of nodes interacting through different network layers. Network robustness, as one of the most successful application areas of complex networks, has attracted great interest in a myriad of research realms. In this regard, how multiplex networks respond to potential attack is still an open issue. Here we study the robustness of multiplex networks under layer node-based random or targeted attack, which means that nodes just suffer attacks in a given layer yet no additional influence to their connections beyond this layer. A theoretical analysis framework is proposed to calculate the critical threshold and the size of giant component of multiplex networks when nodes are removed randomly or intentionally. Via numerous simulations, it is unveiled that the theoretical method can accurately predict the threshold and the size of giant component, irrespective of attack strategies. Moreover, we also compare the robustness of multiplex networks under multiplex node-based attack and layer node-based attack, and find that layer node-based attack makes multiplex networks more vulnerable, regardless of average degree and underlying topology.

Diffusion in random networks: Asymptotic properties, and numerical and engineering approximations

NASA Astrophysics Data System (ADS)

Padrino, Juan C.; Zhang, Duan Z.

2016-11-01

The ensemble phase averaging technique is applied to model mass transport by diffusion in random networks. The system consists of an ensemble of random networks, where each network is made of a set of pockets connected by tortuous channels. Inside a channel, we assume that fluid transport is governed by the one-dimensional diffusion equation. Mass balance leads to an integro-differential equation for the pores mass density. The so-called dual porosity model is found to be equivalent to the leading order approximation of the integration kernel when the diffusion time scale inside the channels is small compared to the macroscopic time scale. As a test problem, we consider the one-dimensional mass diffusion in a semi-infinite domain, whose solution is sought numerically. Because of the required time to establish the linear concentration profile inside a channel, for early times the similarity variable is xt- 1 / 4 rather than xt- 1 / 2 as in the traditional theory. This early time sub-diffusive similarity can be explained by random walk theory through the network. In addition, by applying concepts of fractional calculus, we show that, for small time, the governing equation reduces to a fractional diffusion equation with known solution. We recast this solution in terms of special functions easier to compute. Comparison of the numerical and exact solutions shows excellent agreement.

Random sampling of elementary flux modes in large-scale metabolic networks.

PubMed

Machado, Daniel; Soons, Zita; Patil, Kiran Raosaheb; Ferreira, Eugénio C; Rocha, Isabel

2012-09-15

The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. dmachado@deb.uminho.pt Supplementary data are available at Bioinformatics online.

Epidemics in networks: a master equation approach

NASA Astrophysics Data System (ADS)

Cotacallapa, M.; Hase, M. O.

2016-02-01

A problem closely related to epidemiology, where a subgraph of ‘infected’ links is defined inside a larger network, is investigated. This subgraph is generated from the underlying network by a random variable, which decides whether a link is able to propagate a disease/information. The relaxation timescale of this random variable is examined in both annealed and quenched limits, and the effectiveness of propagation of disease/information is analyzed. The dynamics of the model is governed by a master equation and two types of underlying network are considered: one is scale-free and the other has exponential degree distribution. We have shown that the relaxation timescale of the contagion variable has a major influence on the topology of the subgraph of infected links, which determines the efficiency of spreading of disease/information over the network.

The Value of Information in Distributed Decision Networks

DTIC Science & Technology

2016-03-04

formulation, and then we describe the various results at- tained. 1 Mathematical description of Distributed Decision Network un- der Information...Constraints We now define a mathematical framework for networks. Let G = (V,E) be an undirected random network (graph) drawn from a known distribution pG, 1

Disease Surveillance on Complex Social Networks.

PubMed

Herrera, Jose L; Srinivasan, Ravi; Brownstein, John S; Galvani, Alison P; Meyers, Lauren Ancel

2016-07-01

As infectious disease surveillance systems expand to include digital, crowd-sourced, and social network data, public health agencies are gaining unprecedented access to high-resolution data and have an opportunity to selectively monitor informative individuals. Contact networks, which are the webs of interaction through which diseases spread, determine whether and when individuals become infected, and thus who might serve as early and accurate surveillance sensors. Here, we evaluate three strategies for selecting sensors-sampling the most connected, random, and friends of random individuals-in three complex social networks-a simple scale-free network, an empirical Venezuelan college student network, and an empirical Montreal wireless hotspot usage network. Across five different surveillance goals-early and accurate detection of epidemic emergence and peak, and general situational awareness-we find that the optimal choice of sensors depends on the public health goal, the underlying network and the reproduction number of the disease (R0). For diseases with a low R0, the most connected individuals provide the earliest and most accurate information about both the onset and peak of an outbreak. However, identifying network hubs is often impractical, and they can be misleading if monitored for general situational awareness, if the underlying network has significant community structure, or if R0 is high or unknown. Taking a theoretical approach, we also derive the optimal surveillance system for early outbreak detection but find that real-world identification of such sensors would be nearly impossible. By contrast, the friends-of-random strategy offers a more practical and robust alternative. It can be readily implemented without prior knowledge of the network, and by identifying sensors with higher than average, but not the highest, epidemiological risk, it provides reasonably early and accurate information.

Effect of packing method on the randomness of disc packings

NASA Astrophysics Data System (ADS)

Zhang, Z. P.; Yu, A. B.; Oakeshott, R. B. S.

1996-06-01

The randomness of disc packings, generated by random sequential adsorption (RSA), random packing under gravity (RPG) and Mason packing (MP) which gives a packing density close to that of the RSA packing, has been analysed, based on the Delaunay tessellation, and is evaluated at two levels, i.e. the randomness at individual subunit level which relates to the construction of a triangle from a given edge length distribution and the randomness at network level which relates to the connection between triangles from a given triangle frequency distribution. The Delaunay tessellation itself is also analysed and its almost perfect randomness at the two levels is demonstrated, which verifies the proposed approach and provides a random reference system for the present analysis. It is found that (i) the construction of a triangle subunit is not random for the RSA, MP and RPG packings, with the degree of randomness decreasing from the RSA to MP and then to RPG packing; (ii) the connection of triangular subunits in the network is almost perfectly random for the RSA packing, acceptable for the MP packing and not good for the RPG packing. Packing method is an important factor governing the randomness of disc packings.

Metal availability and the expanding network of microbial metabolisms in the Archaean eon

NASA Astrophysics Data System (ADS)

Moore, Eli K.; Jelen, Benjamin I.; Giovannelli, Donato; Raanan, Hagai; Falkowski, Paul G.

2017-09-01

Life is based on energy gained by electron-transfer processes; these processes rely on oxidoreductase enzymes, which often contain transition metals in their structures. The availability of different metals and substrates has changed over the course of Earth's history as a result of secular changes in redox conditions, particularly global oxygenation. New metabolic pathways using different transition metals co-evolved alongside changing redox conditions. Sulfur reduction, sulfate reduction, methanogenesis and anoxygenic photosynthesis appeared between about 3.8 and 3.4 billion years ago. The oxidoreductases responsible for these metabolisms incorporated metals that were readily available in Archaean oceans, chiefly iron and iron-sulfur clusters. Oxygenic photosynthesis appeared between 3.2 and 2.5 billion years ago, as did methane oxidation, nitrogen fixation, nitrification and denitrification. These metabolisms rely on an expanded range of transition metals presumably made available by the build-up of molecular oxygen in soil crusts and marine microbial mats. The appropriation of copper in enzymes before the Great Oxidation Event is particularly important, as copper is key to nitrogen and methane cycling and was later incorporated into numerous aerobic metabolisms. We find that the diversity of metals used in oxidoreductases has increased through time, suggesting that surface redox potential and metal incorporation influenced the evolution of metabolism, biological electron transfer and microbial ecology.

Universal biology and the statistical mechanics of early life.

PubMed

Goldenfeld, Nigel; Biancalani, Tommaso; Jafarpour, Farshid

2017-12-28

All known life on the Earth exhibits at least two non-trivial common features: the canonical genetic code and biological homochirality, both of which emerged prior to the Last Universal Common Ancestor state. This article describes recent efforts to provide a narrative of this epoch using tools from statistical mechanics. During the emergence of self-replicating life far from equilibrium in a period of chemical evolution, minimal models of autocatalysis show that homochirality would have necessarily co-evolved along with the efficiency of early-life self-replicators. Dynamical system models of the evolution of the genetic code must explain its universality and its highly refined error-minimization properties. These have both been accounted for in a scenario where life arose from a collective, networked phase where there was no notion of species and perhaps even individuality itself. We show how this phase ultimately terminated during an event sometimes known as the Darwinian transition, leading to the present epoch of tree-like vertical descent of organismal lineages. These examples illustrate concrete examples of universal biology: the quest for a fundamental understanding of the basic properties of living systems, independent of precise instantiation in chemistry or other media.This article is part of the themed issue 'Reconceptualizing the origins of life'. © 2017 The Author(s).

Universal biology and the statistical mechanics of early life

NASA Astrophysics Data System (ADS)

Goldenfeld, Nigel; Biancalani, Tommaso; Jafarpour, Farshid

2017-11-01

All known life on the Earth exhibits at least two non-trivial common features: the canonical genetic code and biological homochirality, both of which emerged prior to the Last Universal Common Ancestor state. This article describes recent efforts to provide a narrative of this epoch using tools from statistical mechanics. During the emergence of self-replicating life far from equilibrium in a period of chemical evolution, minimal models of autocatalysis show that homochirality would have necessarily co-evolved along with the efficiency of early-life self-replicators. Dynamical system models of the evolution of the genetic code must explain its universality and its highly refined error-minimization properties. These have both been accounted for in a scenario where life arose from a collective, networked phase where there was no notion of species and perhaps even individuality itself. We show how this phase ultimately terminated during an event sometimes known as the Darwinian transition, leading to the present epoch of tree-like vertical descent of organismal lineages. These examples illustrate concrete examples of universal biology: the quest for a fundamental understanding of the basic properties of living systems, independent of precise instantiation in chemistry or other media. This article is part of the themed issue 'Reconceptualizing the origins of life'.

Fast generation of sparse random kernel graphs

DOE PAGES

Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

2015-09-10

The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

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.

Heterogeneous network promotes species coexistence: metapopulation model for rock-paper-scissors game.

PubMed

Nagatani, Takashi; Ichinose, Genki; Tainaka, Kei-Ichi

2018-05-04

Understanding mechanisms of biodiversity has been a central question in ecology. The coexistence of three species in rock-paper-scissors (RPS) systems are discussed by many authors; however, the relation between coexistence and network structure is rarely discussed. Here we present a metapopulation model for RPS game. The total population is assumed to consist of three subpopulations (nodes). Each individual migrates by random walk; the destination of migration is randomly determined. From reaction-migration equations, we obtain the population dynamics. It is found that the dynamic highly depends on network structures. When a network is homogeneous, the dynamics are neutrally stable: each node has a periodic solution, and the oscillations synchronize in all nodes. However, when a network is heterogeneous, the dynamics approach stable focus and all nodes reach equilibriums with different densities. Hence, the heterogeneity of the network promotes biodiversity.

Robustness of Controllability for Networks Based on Edge-Attack

PubMed Central

Nie, Sen; Wang, Xuwen; Zhang, Haifeng; Li, Qilang; Wang, Binghong

2014-01-01

We study the controllability of networks in the process of cascading failures under two different attacking strategies, random and intentional attack, respectively. For the highest-load edge attack, it is found that the controllability of Erdős-Rényi network, that with moderate average degree, is less robust, whereas the Scale-free network with moderate power-law exponent shows strong robustness of controllability under the same attack strategy. The vulnerability of controllability under random and intentional attacks behave differently with the increasing of removal fraction, especially, we find that the robustness of control has important role in cascades for large removal fraction. The simulation results show that for Scale-free networks with various power-law exponents, the network has larger scale of cascades do not mean that there will be more increments of driver nodes. Meanwhile, the number of driver nodes in cascading failures is also related to the edges amount in strongly connected components. PMID:24586507

Robustness of controllability for networks based on edge-attack.

PubMed

Nie, Sen; Wang, Xuwen; Zhang, Haifeng; Li, Qilang; Wang, Binghong

2014-01-01

We study the controllability of networks in the process of cascading failures under two different attacking strategies, random and intentional attack, respectively. For the highest-load edge attack, it is found that the controllability of Erdős-Rényi network, that with moderate average degree, is less robust, whereas the Scale-free network with moderate power-law exponent shows strong robustness of controllability under the same attack strategy. The vulnerability of controllability under random and intentional attacks behave differently with the increasing of removal fraction, especially, we find that the robustness of control has important role in cascades for large removal fraction. The simulation results show that for Scale-free networks with various power-law exponents, the network has larger scale of cascades do not mean that there will be more increments of driver nodes. Meanwhile, the number of driver nodes in cascading failures is also related to the edges amount in strongly connected components.

Selective pinning control of the average disease transmissibility in an HIV contact network

NASA Astrophysics Data System (ADS)

du Toit, E. F.; Craig, I. K.

2015-07-01

Medication is applied to the HIV-infected nodes of high-risk contact networks with the aim of controlling the spread of disease to a predetermined maximum level. This intervention, known as pinning control, is performed both selectively and randomly in the network. These strategies are applied to 300 independent realizations per reference level of incidence on connected undirectional networks without isolated components and varying in size from 100 to 10 000 nodes per network. It is shown that a selective on-off pinning control strategy can control the networks studied with limited steady-state error and, comparing the medians of the doses from both strategies, uses 51.3% less medication than random pinning of all infected nodes. Selective pinning could possibly be used by public health specialists to identify the maximum level of HIV incidence in a population that can be achieved in a constrained funding environment.

Tests of peak flow scaling in simulated self-similar river networks

USGS Publications Warehouse

Menabde, M.; Veitzer, S.; Gupta, V.; Sivapalan, M.

2001-01-01

The effect of linear flow routing incorporating attenuation and network topology on peak flow scaling exponent is investigated for an instantaneously applied uniform runoff on simulated deterministic and random self-similar channel networks. The flow routing is modelled by a linear mass conservation equation for a discrete set of channel links connected in parallel and series, and having the same topology as the channel network. A quasi-analytical solution for the unit hydrograph is obtained in terms of recursion relations. The analysis of this solution shows that the peak flow has an asymptotically scaling dependence on the drainage area for deterministic Mandelbrot-Vicsek (MV) and Peano networks, as well as for a subclass of random self-similar channel networks. However, the scaling exponent is shown to be different from that predicted by the scaling properties of the maxima of the width functions. ?? 2001 Elsevier Science Ltd. All rights reserved.

Robustness and fragility in coupled oscillator networks under targeted attacks.

PubMed

Yuan, Tianyu; Aihara, Kazuyuki; Tanaka, Gouhei

2017-01-01

The dynamical tolerance of coupled oscillator networks against local failures is studied. As the fraction of failed oscillator nodes gradually increases, the mean oscillation amplitude in the entire network decreases and then suddenly vanishes at a critical fraction as a phase transition. This critical fraction, widely used as a measure of the network robustness, was analytically derived for random failures but not for targeted attacks so far. Here we derive the general formula for the critical fraction, which can be applied to both random failures and targeted attacks. We consider the effects of targeting oscillator nodes based on their degrees. First we deal with coupled identical oscillators with homogeneous edge weights. Then our theory is applied to networks with heterogeneous edge weights and to those with nonidentical oscillators. The analytical results are validated by numerical experiments. Our results reveal the key factors governing the robustness and fragility of oscillator networks.

Large-size, high-uniformity, random silver nanowire networks as transparent electrodes for crystalline silicon wafer solar cells.

PubMed

Xie, Shouyi; Ouyang, Zi; Jia, Baohua; Gu, Min

2013-05-06

Metal nanowire networks are emerging as next generation transparent electrodes for photovoltaic devices. We demonstrate the application of random silver nanowire networks as the top electrode on crystalline silicon wafer solar cells. The dependence of transmittance and sheet resistance on the surface coverage is measured. Superior optical and electrical properties are observed due to the large-size, highly-uniform nature of these networks. When applying the nanowire networks on the solar cells with an optimized two-step annealing process, we achieved as large as 19% enhancement on the energy conversion efficiency. The detailed analysis reveals that the enhancement is mainly caused by the improved electrical properties of the solar cells due to the silver nanowire networks. Our result reveals that this technology is a promising alternative transparent electrode technology for crystalline silicon wafer solar cells.

Improved Neural Networks with Random Weights for Short-Term Load Forecasting

PubMed Central

Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo

2015-01-01

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting. PMID:26629825

Improved Neural Networks with Random Weights for Short-Term Load Forecasting.

PubMed

Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo

2015-01-01

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.

Random catalytic reaction networks

NASA Astrophysics Data System (ADS)

Stadler, Peter F.; Fontana, Walter; Miller, John H.

1993-03-01

We study networks that are a generalization of replicator (or Lotka-Volterra) equations. They model the dynamics of a population of object types whose binary interactions determine the specific type of interaction product. Such a system always reduces its dimension to a subset that contains production pathways for all of its members. The network equation can be rewritten at a level of collectives in terms of two basic interaction patterns: replicator sets and cyclic transformation pathways among sets. Although the system contains well-known cases that exhibit very complicated dynamics, the generic behavior of randomly generated systems is found (numerically) to be extremely robust: convergence to a globally stable rest point. It is easy to tailor networks that display replicator interactions where the replicators are entire self-sustaining subsystems, rather than structureless units. A numerical scan of random systems highlights the special properties of elementary replicators: they reduce the effective interconnectedness of the system, resulting in enhanced competition, and strong correlations between the concentrations.

A Scientific Note on the Lactic Acid Bacterial Flora Discovered in the Honey Stomach of Swedish honey bees - a continuing study on honey bees in the U.S.A.

USDA-ARS?s Scientific Manuscript database

Beneficial bacteria have been found in honey stomachs of the honey bee, Apis mellifera; a unique flora that appears to have coevolved with the honey bees. The health of our most important pollinators has come into focus during the last few years, because of yet unexplained conditions and diseases t...

Understanding the compatibility of multiple uses on forest land: a survey of multiresource research with application to the Pacific Northwest.

Treesearch

James A. Stevens; Claire A. Montgomery

2002-01-01

In this report, multiresource research is described as it has coevolved with forest policy objectives—from managing for single or dominant uses, to managing for compatible multiple forest uses, to sustaining ecosystem health on the forest. The evolution of analytical methods for multiresource research is traced from impact analysis to multiresource modeling, and...

Dynamic Analytical Capability to Better Understand and Anticipate Extremist Shifts Within Populations under Authoritarian Regimes.

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

Bernard, Michael Lewis

2015-11-01

The purpose of this work is to create a generalizable data- and theory-supported capability to better understand and anticipate (with quantifiable uncertainty): 1) how the dynamics of allegiance formations between various groups and society are impacted by active conflict and by third-party interventions and 2) how/why extremist allegiances co-evolve over time due to changing geopolitical, sociocultural, and military conditions.

Fast Algorithms for Mining Co-evolving Time Series

DTIC Science & Technology

2011-09-01

Keogh et al., 2001, 2004] and (b) forecasting, like an autoregressive integrated moving average model ( ARIMA ) and related meth- ods [Box et al., 1994...computing hardware? We develop models to mine time series with missing values, to extract compact representation from time sequences, to segment the...sequences, and to do forecasting. For large scale data, we propose algorithms for learning time series models , in particular, including Linear Dynamical

The `I see you' prey-predator signal of Apis cerana is innate

NASA Astrophysics Data System (ADS)

Tan, Ken; Wang, Zhenwei; Chen, Weiweng; Hu, Zongwen; Oldroyd, Benjamin P.

2013-03-01

An `I see you' (ISY) prey-predator signal can co-evolve when such a signal benefits both prey and predator. The prey benefits if, by producing the signal, the predator is likely to break off an attack. The predator benefits if it is informed by the signal that the prey is aware of its presence and can break off what is likely to be an unsuccessful and potentially costly hunt. Because the signal and response co-evolve in two species, the behaviour underlying an ISY signal is expected to have a strong genetic component and cannot be entirely learned. An example of an ISY signal is the `shimmering' behaviour performed by Asian hive bee workers in the presence of their predator Vespa velutina. To test the prediction that bee-hornet signalling is heritable, we let honey bee workers of two species emerge in an incubator so that they had never been exposed to V. velutina. In Apis cerana, the shimmering response developed 48 h post-emergence, was strong after 72 h and increased further over 2 weeks. In contrast, A. mellifera, which has evolved in the absence of Asian hornets, did not produce the shimmering signal. In control tests, A. cerana workers exposed to a non-threatening butterfly did not respond with the shimmering signal.

Signal design and courtship presentation coincide for highly biased delivery of an iridescent butterfly mating signal

PubMed Central

White, Thomas E; Zeil, Jochen; Kemp, Darrell J; Rodriguez, R; Lenormand, T

2015-01-01

Sensory drive theory contends that signaling systems should evolve to optimize transmission between senders and intended receivers, while minimizing visibility to eavesdroppers where possible. In visual communication systems, the high directionality afforded by iridescent coloration presents underappreciated avenues for mediating this trade-off. This hypothesis predicts functional links between signal design and presentation such that visual conspicuousness is maximized only under ecologically relevant settings and/or to select audiences. We addressed this prediction using Hypolimnas bolina, a butterfly in which males possess ultraviolet markings on their dorsal wing surfaces with a narrow angular reflectance function. Males bearing brighter dorsal markings are increasingly attractive to females, but also likely more conspicuous to predators. Our data indicate that, during courtship (and given the ritualized wingbeat dynamics at these times), males position themselves relative to females in such a way as to simultaneously maximize three components of known or putative signal conspicuousness: brightness, area, and iridescent flash. This suggests that male signal design and display have coevolved for the delivery of an optimally conspicuous signal to courted females. More broadly, these findings imply a potential signaling role for iridescence itself, and pose a novel example for how signal design may coevolve with the behavioral context of display. PMID:25328995

Host specificity, phenotype matching and the evolution of reproductive isolation in a coevolved plant-pollinator mutualism.

PubMed

Himler, Anna G; Machado, Carlos A

2009-12-01

Coevolutionary interactions between plants and their associated pollinators and seed dispersers are thought to have promoted the diversification of flowering plants (Raven 1977; Regal 1977; Stebbins 1981). The actual mechanisms by which pollinators could drive species diversification in plants are not fully understood. However, it is thought that pollinator host specialization can influence the evolution of reproductive isolation among plant populations because the pollinator's choice of host is what determines patterns of gene flow in its host plant, and host choice may also have important consequences on pollinator and host fitness (Grant 1949; Bawa 1992). In this issue of Molecular Ecology, Smith et al. (2009) present a very interesting study that addresses how host specialization affects pollinator fitness and patterns of gene flow in a plant host. Several aspects of this study match elements of a seminal mathematical model of plant-pollinator codivergence (Kiester et al. 1984) suggesting that reciprocal selection for matched plant and pollinator reproductive traits may lead to speciation in the host and its pollinator when there is strong host specialization and a pattern of geographic subdivision. Smith et al.'s study represents an important step to fill the gap in our understanding of how reciprocal selection may lead to speciation in coevolved plant-pollinator mutualisms.

Translation fidelity coevolves with longevity.

PubMed

Ke, Zhonghe; Mallik, Pramit; Johnson, Adam B; Luna, Facundo; Nevo, Eviatar; Zhang, Zhengdong D; Gladyshev, Vadim N; Seluanov, Andrei; Gorbunova, Vera

2017-10-01

Whether errors in protein synthesis play a role in aging has been a subject of intense debate. It has been suggested that rare mistakes in protein synthesis in young organisms may result in errors in the protein synthesis machinery, eventually leading to an increasing cascade of errors as organisms age. Studies that followed generally failed to identify a dramatic increase in translation errors with aging. However, whether translation fidelity plays a role in aging remained an open question. To address this issue, we examined the relationship between translation fidelity and maximum lifespan across 17 rodent species with diverse lifespans. To measure translation fidelity, we utilized sensitive luciferase-based reporter constructs with mutations in an amino acid residue critical to luciferase activity, wherein misincorporation of amino acids at this mutated codon re-activated the luciferase. The frequency of amino acid misincorporation at the first and second codon positions showed strong negative correlation with maximum lifespan. This correlation remained significant after phylogenetic correction, indicating that translation fidelity coevolves with longevity. These results give new life to the role of protein synthesis errors in aging: Although the error rate may not significantly change with age, the basal rate of translation errors is important in defining lifespan across mammals. © 2017 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.

Within-population covariation between sexual reproduction and susceptibility to local parasites.

PubMed

Gibson, Amanda K; Xu, Julie Y; Lively, Curtis M

2016-09-01

Evolutionary biology has yet to reconcile the ubiquity of sex with its costs relative to asexual reproduction. Here, we test the hypothesis that coevolving parasites maintain sex in their hosts. Specifically, we examined the distributions of sexual reproduction and susceptibility to local parasites within a single population of freshwater snails (Potamopyrgus antipodarum). Susceptibility to local trematode parasites (Microphallus sp.) is a relative measure of the strength of coevolutionary selection in this system. Thus, if coevolving parasites maintain sex, sexual snails should be common where susceptibility is high. We tested this prediction in a mixed population of sexual and asexual snails by measuring the susceptibility of snails from multiple sites in a lake. Consistent with the prediction, the frequency of sexual snails was tightly and positively correlated with susceptibility to local parasites. Strikingly, in just two years, asexual females increased in frequency at sites where susceptibility declined. We also found that the frequency of sexual females covaries more strongly with susceptibility than with the prevalence of Microphallus infection in the field. In linking susceptibility to the frequency of sexual hosts, our results directly implicate spatial variation in coevolutionary selection in driving the geographic mosaic of sex. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

PubMed

Wen, Guihua; Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

2017-01-01

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

Random Deep Belief Networks for Recognizing Emotions from Speech Signals

PubMed Central

Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

2017-01-01

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition. PMID:28356908

Preserved Network Metrics across Translated Texts

NASA Astrophysics Data System (ADS)

Cabatbat, Josephine Jill T.; Monsanto, Jica P.; Tapang, Giovanni A.

2014-09-01

Co-occurrence language networks based on Bible translations and the Universal Declaration of Human Rights (UDHR) translations in different languages were constructed and compared with random text networks. Among the considered network metrics, the network size, N, the normalized betweenness centrality (BC), and the average k-nearest neighbors, knn, were found to be the most preserved across translations. Moreover, similar frequency distributions of co-occurring network motifs were observed for translated texts networks.

Predictive Coding: A Possible Explanation of Filling-In at the Blind Spot

PubMed Central

Raman, Rajani; Sarkar, Sandip

2016-01-01

Filling-in at the blind spot is a perceptual phenomenon in which the visual system fills the informational void, which arises due to the absence of retinal input corresponding to the optic disc, with surrounding visual attributes. It is known that during filling-in, nonlinear neural responses are observed in the early visual area that correlates with the perception, but the knowledge of underlying neural mechanism for filling-in at the blind spot is far from complete. In this work, we attempted to present a fresh perspective on the computational mechanism of filling-in process in the framework of hierarchical predictive coding, which provides a functional explanation for a range of neural responses in the cortex. We simulated a three-level hierarchical network and observe its response while stimulating the network with different bar stimulus across the blind spot. We find that the predictive-estimator neurons that represent blind spot in primary visual cortex exhibit elevated non-linear response when the bar stimulated both sides of the blind spot. Using generative model, we also show that these responses represent the filling-in completion. All these results are consistent with the finding of psychophysical and physiological studies. In this study, we also demonstrate that the tolerance in filling-in qualitatively matches with the experimental findings related to non-aligned bars. We discuss this phenomenon in the predictive coding paradigm and show that all our results could be explained by taking into account the efficient coding of natural images along with feedback and feed-forward connections that allow priors and predictions to co-evolve to arrive at the best prediction. These results suggest that the filling-in process could be a manifestation of the general computational principle of hierarchical predictive coding of natural images. PMID:26959812

Network Sampling with Memory: A proposal for more efficient sampling from social networks.

PubMed

Mouw, Ted; Verdery, Ashton M

2012-08-01

Techniques for sampling from networks have grown into an important area of research across several fields. For sociologists, the possibility of sampling from a network is appealing for two reasons: (1) A network sample can yield substantively interesting data about network structures and social interactions, and (2) it is useful in situations where study populations are difficult or impossible to survey with traditional sampling approaches because of the lack of a sampling frame. Despite its appeal, methodological concerns about the precision and accuracy of network-based sampling methods remain. In particular, recent research has shown that sampling from a network using a random walk based approach such as Respondent Driven Sampling (RDS) can result in high design effects (DE)-the ratio of the sampling variance to the sampling variance of simple random sampling (SRS). A high design effect means that more cases must be collected to achieve the same level of precision as SRS. In this paper we propose an alternative strategy, Network Sampling with Memory (NSM), which collects network data from respondents in order to reduce design effects and, correspondingly, the number of interviews needed to achieve a given level of statistical power. NSM combines a "List" mode, where all individuals on the revealed network list are sampled with the same cumulative probability, with a "Search" mode, which gives priority to bridge nodes connecting the current sample to unexplored parts of the network. We test the relative efficiency of NSM compared to RDS and SRS on 162 school and university networks from Add Health and Facebook that range in size from 110 to 16,278 nodes. The results show that the average design effect for NSM on these 162 networks is 1.16, which is very close to the efficiency of a simple random sample (DE=1), and 98.5% lower than the average DE we observed for RDS.

Network Sampling with Memory: A proposal for more efficient sampling from social networks

PubMed Central

Mouw, Ted; Verdery, Ashton M.

2013-01-01

Techniques for sampling from networks have grown into an important area of research across several fields. For sociologists, the possibility of sampling from a network is appealing for two reasons: (1) A network sample can yield substantively interesting data about network structures and social interactions, and (2) it is useful in situations where study populations are difficult or impossible to survey with traditional sampling approaches because of the lack of a sampling frame. Despite its appeal, methodological concerns about the precision and accuracy of network-based sampling methods remain. In particular, recent research has shown that sampling from a network using a random walk based approach such as Respondent Driven Sampling (RDS) can result in high design effects (DE)—the ratio of the sampling variance to the sampling variance of simple random sampling (SRS). A high design effect means that more cases must be collected to achieve the same level of precision as SRS. In this paper we propose an alternative strategy, Network Sampling with Memory (NSM), which collects network data from respondents in order to reduce design effects and, correspondingly, the number of interviews needed to achieve a given level of statistical power. NSM combines a “List” mode, where all individuals on the revealed network list are sampled with the same cumulative probability, with a “Search” mode, which gives priority to bridge nodes connecting the current sample to unexplored parts of the network. We test the relative efficiency of NSM compared to RDS and SRS on 162 school and university networks from Add Health and Facebook that range in size from 110 to 16,278 nodes. The results show that the average design effect for NSM on these 162 networks is 1.16, which is very close to the efficiency of a simple random sample (DE=1), and 98.5% lower than the average DE we observed for RDS. PMID:24159246

Programmability of nanowire networks

NASA Astrophysics Data System (ADS)

Bellew, A. T.; Bell, A. P.; McCarthy, E. K.; Fairfield, J. A.; Boland, J. J.

2014-07-01

Electrical connectivity in networks of nanoscale junctions must be better understood if nanowire devices are to be scaled up from single wires to functional material systems. We show that the natural connectivity behaviour found in random nanowire networks presents a new paradigm for creating multi-functional, programmable materials. In devices made from networks of Ni/NiO core-shell nanowires at different length scales, we discover the emergence of distinct behavioural regimes when networks are electrically stressed. We show that a small network, with few nanowire-nanowire junctions, acts as a unipolar resistive switch, demonstrating very high ON/OFF current ratios (>105). However, large networks of nanowires distribute an applied bias across a large number of junctions, and thus respond not by switching but instead by evolving connectivity. We demonstrate that these emergent properties lead to fault-tolerant materials whose resistance may be tuned, and which are capable of adaptively reconfiguring under stress. By combining these two behavioural regimes, we demonstrate that the same nanowire network may be programmed to act both as a metallic interconnect, and a resistive switch device with high ON/OFF ratio. These results enable the fabrication of programmable, multi-functional materials from random nanowire networks.Electrical connectivity in networks of nanoscale junctions must be better understood if nanowire devices are to be scaled up from single wires to functional material systems. We show that the natural connectivity behaviour found in random nanowire networks presents a new paradigm for creating multi-functional, programmable materials. In devices made from networks of Ni/NiO core-shell nanowires at different length scales, we discover the emergence of distinct behavioural regimes when networks are electrically stressed. We show that a small network, with few nanowire-nanowire junctions, acts as a unipolar resistive switch, demonstrating very high ON/OFF current ratios (>105). However, large networks of nanowires distribute an applied bias across a large number of junctions, and thus respond not by switching but instead by evolving connectivity. We demonstrate that these emergent properties lead to fault-tolerant materials whose resistance may be tuned, and which are capable of adaptively reconfiguring under stress. By combining these two behavioural regimes, we demonstrate that the same nanowire network may be programmed to act both as a metallic interconnect, and a resistive switch device with high ON/OFF ratio. These results enable the fabrication of programmable, multi-functional materials from random nanowire networks. Electronic supplementary information (ESI) available: Nanowire statistics (length, diameter statistics, and oxide thickness) are provided. Forming curves for single junctions and networks. Passive voltage contrast image demonstrating selectivity of conductive pathways in 100 μm network. See DOI: 10.1039/c4nr02338b

Robustness and Vulnerability of Networks with Dynamical Dependency Groups.

PubMed

Bai, Ya-Nan; Huang, Ning; Wang, Lei; Wu, Zhi-Xi

2016-11-28

The dependency property and self-recovery of failure nodes both have great effects on the robustness of networks during the cascading process. Existing investigations focused mainly on the failure mechanism of static dependency groups without considering the time-dependency of interdependent nodes and the recovery mechanism in reality. In this study, we present an evolving network model consisting of failure mechanisms and a recovery mechanism to explore network robustness, where the dependency relations among nodes vary over time. Based on generating function techniques, we provide an analytical framework for random networks with arbitrary degree distribution. In particular, we theoretically find that an abrupt percolation transition exists corresponding to the dynamical dependency groups for a wide range of topologies after initial random removal. Moreover, when the abrupt transition point is above the failure threshold of dependency groups, the evolving network with the larger dependency groups is more vulnerable; when below it, the larger dependency groups make the network more robust. Numerical simulations employing the Erdős-Rényi network and Barabási-Albert scale free network are performed to validate our theoretical results.

Approximating natural connectivity of scale-free networks based on largest eigenvalue

NASA Astrophysics Data System (ADS)

Tan, S.-Y.; Wu, J.; Li, M.-J.; Lu, X.

2016-06-01

It has been recently proposed that natural connectivity can be used to efficiently characterize the robustness of complex networks. The natural connectivity has an intuitive physical meaning and a simple mathematical formulation, which corresponds to an average eigenvalue calculated from the graph spectrum. However, as a network model close to the real-world system that widely exists, the scale-free network is found difficult to obtain its spectrum analytically. In this article, we investigate the approximation of natural connectivity based on the largest eigenvalue in both random and correlated scale-free networks. It is demonstrated that the natural connectivity of scale-free networks can be dominated by the largest eigenvalue, which can be expressed asymptotically and analytically to approximate natural connectivity with small errors. Then we show that the natural connectivity of random scale-free networks increases linearly with the average degree given the scaling exponent and decreases monotonically with the scaling exponent given the average degree. Moreover, it is found that, given the degree distribution, the more assortative a scale-free network is, the more robust it is. Experiments in real networks validate our methods and results.

Counting and classifying attractors in high dimensional dynamical systems.

PubMed

Bagley, R J; Glass, L

1996-12-07

Randomly connected Boolean networks have been used as mathematical models of neural, genetic, and immune systems. A key quantity of such networks is the number of basins of attraction in the state space. The number of basins of attraction changes as a function of the size of the network, its connectivity and its transition rules. In discrete networks, a simple count of the number of attractors does not reveal the combinatorial structure of the attractors. These points are illustrated in a reexamination of dynamics in a class of random Boolean networks considered previously by Kauffman. We also consider comparisons between dynamics in discrete networks and continuous analogues. A continuous analogue of a discrete network may have a different number of attractors for many different reasons. Some attractors in discrete networks may be associated with unstable dynamics, and several different attractors in a discrete network may be associated with a single attractor in the continuous case. Special problems in determining attractors in continuous systems arise when there is aperiodic dynamics associated with quasiperiodicity of deterministic chaos.

Investigating Supermassive Black Hole Spin at Different Redshift

NASA Astrophysics Data System (ADS)

Sinanan-Singh, Jasmine

2018-01-01

Supermassive black hole (SMBH) spin encodes vital information about the history of SMBH growth. High spins indicate a history of growth through large mass accretion events, which spin-up the black hole; Intermediate spins indicate a history of galactic mergers, which don't tend to systemcatically spin-up or spin-down black holes; low spins are attributed to successive, small accretion events with random orientations. Examining spin over different redshifts will help us understand the relative growth of SMBHs by mergers or accretion over cosmic time, an important part of understanding how SMBHs and their host galaxies co-evolved over time. To study spin, we compute the Fe K alpha emission line from the X-ray spectra of AGN sources in the Chandra-COSMOS Legacy Survey. We stack rest frame AGN spectra to improve the signal-to-noise ratio since the photon counts are low for individual spectra, and then average the spectra using an unwieghted mean. Our method is derived from Corral et al. (2008). We test our method on the two brightest sources in the COSMOS Survey and compute the rest frame average Fe K alpha emission line for different redshift bins. The SAO REU program is funded by the National Science Foundation REU and Department of Defense ASSURE programs under NSF Grant AST-1659473, and by the Smithsonian Institution.

Massive-scale gene co-expression network construction and robustness testing using random matrix theory.

PubMed

Gibson, Scott M; Ficklin, Stephen P; Isaacson, Sven; Luo, Feng; Feltus, Frank A; Smith, Melissa C

2013-01-01

The study of gene relationships and their effect on biological function and phenotype is a focal point in systems biology. Gene co-expression networks built using microarray expression profiles are one technique for discovering and interpreting gene relationships. A knowledge-independent thresholding technique, such as Random Matrix Theory (RMT), is useful for identifying meaningful relationships. Highly connected genes in the thresholded network are then grouped into modules that provide insight into their collective functionality. While it has been shown that co-expression networks are biologically relevant, it has not been determined to what extent any given network is functionally robust given perturbations in the input sample set. For such a test, hundreds of networks are needed and hence a tool to rapidly construct these networks. To examine functional robustness of networks with varying input, we enhanced an existing RMT implementation for improved scalability and tested functional robustness of human (Homo sapiens), rice (Oryza sativa) and budding yeast (Saccharomyces cerevisiae). We demonstrate dramatic decrease in network construction time and computational requirements and show that despite some variation in global properties between networks, functional similarity remains high. Moreover, the biological function captured by co-expression networks thresholded by RMT is highly robust.

Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes

NASA Astrophysics Data System (ADS)

Wiedermann, Marc; Donges, Jonathan F.; Kurths, Jürgen; Donner, Reik V.

2016-04-01

Networks with nodes embedded in a metric space have gained increasing interest in recent years. The effects of spatial embedding on the networks' structural characteristics, however, are rarely taken into account when studying their macroscopic properties. Here, we propose a hierarchy of null models to generate random surrogates from a given spatially embedded network that can preserve certain global and local statistics associated with the nodes' embedding in a metric space. Comparing the original network's and the resulting surrogates' global characteristics allows one to quantify to what extent these characteristics are already predetermined by the spatial embedding of the nodes and links. We apply our framework to various real-world spatial networks and show that the proposed models capture macroscopic properties of the networks under study much better than standard random network models that do not account for the nodes' spatial embedding. Depending on the actual performance of the proposed null models, the networks are categorized into different classes. Since many real-world complex networks are in fact spatial networks, the proposed approach is relevant for disentangling the underlying complex system structure from spatial embedding of nodes in many fields, ranging from social systems over infrastructure and neurophysiology to climatology.

Co-extinction in a host-parasite network: identifying key hosts for network stability.

PubMed

Dallas, Tad; Cornelius, Emily

2015-08-17

Parasites comprise a substantial portion of total biodiversity. Ultimately, this means that host extinction could result in many secondary extinctions of obligate parasites and potentially alter host-parasite network structure. Here, we examined a highly resolved fish-parasite network to determine key hosts responsible for maintaining parasite diversity and network structure (quantified here as nestedness and modularity). We evaluated four possible host extinction orders and compared the resulting co-extinction dynamics to random extinction simulations; including host removal based on estimated extinction risk, parasite species richness and host level contributions to nestedness and modularity. We found that all extinction orders, except the one based on realistic extinction risk, resulted in faster declines in parasite diversity and network structure relative to random biodiversity loss. Further, we determined species-level contributions to network structure were best predicted by parasite species richness and host family. Taken together, we demonstrate that a small proportion of hosts contribute substantially to network structure and that removal of these hosts results in rapid declines in parasite diversity and network structure. As network stability can potentially be inferred through measures of network structure, our findings may provide insight into species traits that confer stability.

Improved PPP Ambiguity Resolution Considering the Stochastic Characteristics of Atmospheric Corrections from Regional Networks

PubMed Central

Li, Yihe; Li, Bofeng; Gao, Yang

2015-01-01

With the increased availability of regional reference networks, Precise Point Positioning (PPP) can achieve fast ambiguity resolution (AR) and precise positioning by assimilating the satellite fractional cycle biases (FCBs) and atmospheric corrections derived from these networks. In such processing, the atmospheric corrections are usually treated as deterministic quantities. This is however unrealistic since the estimated atmospheric corrections obtained from the network data are random and furthermore the interpolated corrections diverge from the realistic corrections. This paper is dedicated to the stochastic modelling of atmospheric corrections and analyzing their effects on the PPP AR efficiency. The random errors of the interpolated corrections are processed as two components: one is from the random errors of estimated corrections at reference stations, while the other arises from the atmospheric delay discrepancies between reference stations and users. The interpolated atmospheric corrections are then applied by users as pseudo-observations with the estimated stochastic model. Two data sets are processed to assess the performance of interpolated corrections with the estimated stochastic models. The results show that when the stochastic characteristics of interpolated corrections are properly taken into account, the successful fix rate reaches 93.3% within 5 min for a medium inter-station distance network and 80.6% within 10 min for a long inter-station distance network. PMID:26633400

Effects of multiple spreaders in community networks

NASA Astrophysics Data System (ADS)

Hu, Zhao-Long; Ren, Zhuo-Ming; Yang, Guang-Yong; Liu, Jian-Guo

2014-12-01

Human contact networks exhibit the community structure. Understanding how such community structure affects the epidemic spreading could provide insights for preventing the spreading of epidemics between communities. In this paper, we explore the spreading of multiple spreaders in community networks. A network based on the clustering preferential mechanism is evolved, whose communities are detected by the Girvan-Newman (GN) algorithm. We investigate the spreading effectiveness by selecting the nodes as spreaders in the following ways: nodes with the largest degree in each community (community hubs), the same number of nodes with the largest degree from the global network (global large-degree) and randomly selected one node within each community (community random). The experimental results on the SIR model show that the spreading effectiveness based on the global large-degree and community hubs methods is the same in the early stage of the infection and the method of community random is the worst. However, when the infection rate exceeds the critical value, the global large-degree method embodies the worst spreading effectiveness. Furthermore, the discrepancy of effectiveness for the three methods will decrease as the infection rate increases. Therefore, we should immunize the hubs in each community rather than those hubs in the global network to prevent the outbreak of epidemics.

LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks

NASA Astrophysics Data System (ADS)

Berahmand, Kamal; Bouyer, Asgarali

2018-03-01

Community detection is an essential approach for analyzing the structural and functional properties of complex networks. Although many community detection algorithms have been recently presented, most of them are weak and limited in different ways. Label Propagation Algorithm (LPA) is a well-known and efficient community detection technique which is characterized by the merits of nearly-linear running time and easy implementation. However, LPA has some significant problems such as instability, randomness, and monster community detection. In this paper, an algorithm, namely node’s label influence policy for label propagation algorithm (LP-LPA) was proposed for detecting efficient community structures. LP-LPA measures link strength value for edges and nodes’ label influence value for nodes in a new label propagation strategy with preference on link strength and for initial nodes selection, avoid of random behavior in tiebreak states, and efficient updating order and rule update. These procedures can sort out the randomness issue in an original LPA and stabilize the discovered communities in all runs of the same network. Experiments on synthetic networks and a wide range of real-world social networks indicated that the proposed method achieves significant accuracy and high stability. Indeed, it can obviously solve monster community problem with regard to detecting communities in networks.

Improved PPP Ambiguity Resolution Considering the Stochastic Characteristics of Atmospheric Corrections from Regional Networks.

PubMed

Li, Yihe; Li, Bofeng; Gao, Yang

2015-11-30

With the increased availability of regional reference networks, Precise Point Positioning (PPP) can achieve fast ambiguity resolution (AR) and precise positioning by assimilating the satellite fractional cycle biases (FCBs) and atmospheric corrections derived from these networks. In such processing, the atmospheric corrections are usually treated as deterministic quantities. This is however unrealistic since the estimated atmospheric corrections obtained from the network data are random and furthermore the interpolated corrections diverge from the realistic corrections. This paper is dedicated to the stochastic modelling of atmospheric corrections and analyzing their effects on the PPP AR efficiency. The random errors of the interpolated corrections are processed as two components: one is from the random errors of estimated corrections at reference stations, while the other arises from the atmospheric delay discrepancies between reference stations and users. The interpolated atmospheric corrections are then applied by users as pseudo-observations with the estimated stochastic model. Two data sets are processed to assess the performance of interpolated corrections with the estimated stochastic models. The results show that when the stochastic characteristics of interpolated corrections are properly taken into account, the successful fix rate reaches 93.3% within 5 min for a medium inter-station distance network and 80.6% within 10 min for a long inter-station distance network.

Optimal source coding, removable noise elimination, and natural coordinate system construction for general vector sources using replicator neural networks

NASA Astrophysics Data System (ADS)

Hecht-Nielsen, Robert

1997-04-01

A new universal one-chart smooth manifold model for vector information sources is introduced. Natural coordinates (a particular type of chart) for such data manifolds are then defined. Uniformly quantized natural coordinates form an optimal vector quantization code for a general vector source. Replicator neural networks (a specialized type of multilayer perceptron with three hidden layers) are the introduced. As properly configured examples of replicator networks approach minimum mean squared error (e.g., via training and architecture adjustment using randomly chosen vectors from the source), these networks automatically develop a mapping which, in the limit, produces natural coordinates for arbitrary source vectors. The new concept of removable noise (a noise model applicable to a wide variety of real-world noise processes) is then discussed. Replicator neural networks, when configured to approach minimum mean squared reconstruction error (e.g., via training and architecture adjustment on randomly chosen examples from a vector source, each with randomly chosen additive removable noise contamination), in the limit eliminate removable noise and produce natural coordinates for the data vector portions of the noise-corrupted source vectors. Consideration regarding selection of the dimension of a data manifold source model and the training/configuration of replicator neural networks are discussed.

Quantum games on evolving random networks

NASA Astrophysics Data System (ADS)

Pawela, Łukasz

2016-09-01

We study the advantages of quantum strategies in evolutionary social dilemmas on evolving random networks. We focus our study on the two-player games: prisoner's dilemma, snowdrift and stag-hunt games. The obtained result show the benefits of quantum strategies for the prisoner's dilemma game. For the other two games, we obtain regions of parameters where the quantum strategies dominate, as well as regions where the classical strategies coexist.

A Comprehensive Peer Network Intervention to Improve Social Communication of Children with Autism Spectrum Disorders: A Randomized Trial in Kindergarten and First Grade

ERIC Educational Resources Information Center

Kamps, Debra; Thiemann-Bourque, Kathy; Heitzman-Powell, Linda; Schwartz, Ilene; Rosenberg, Nancy; Mason, Rose; Cox, Suzanne

2015-01-01

The purpose of this randomized control group study was to examine the effects of a peer network intervention that included peer mediation and direct instruction for Kindergarten and First-grade children with autism spectrum disorders. Trained school staff members provided direct instruction for 56 children in the intervention group, and 39…

Influence network linkages across implementation strategy conditions in a randomized controlled trial of two strategies for scaling up evidence-based practices in public youth-serving systems

PubMed Central

2013-01-01

Background Given the importance of influence networks in the implementation of evidence-based practices and interventions, it is unclear whether such networks continue to operate as sources of information and advice when they are segmented and disrupted by randomization to different implementation strategy conditions. The present study examines the linkages across implementation strategy conditions of social influence networks of leaders of youth-serving systems in 12 California counties participating in a randomized controlled trial of community development teams (CDTs) to scale up use of an evidence-based practice. Methods Semi-structured interviews were conducted with 38 directors, assistant directors, and program managers of county probation, mental health, and child welfare departments. A web-based survey collected additional quantitative data on information and advice networks of study participants. A mixed-methods approach to data analysis was used to create a sociometric data set (n = 176) to examine linkages between treatment and standard conditions. Results Of those network members who were affiliated with a county (n = 137), only 6 (4.4%) were directly connected to a member of the opposite implementation strategy condition; 19 (13.9%) were connected by two steps or fewer to a member of the opposite implementation strategy condition; 64 (46.7%) were connected by three or fewer steps to a member of the opposite implementation strategy condition. Most of the indirect steps between individuals who were in different implementation strategy conditions were connections involving a third non-county organizational entity that had an important role in the trial in keeping the implementation strategy conditions separate. When these entities were excluded, the CDT network exhibited fewer components and significantly higher betweenness centralization than did the standard condition network. Conclusion Although the integrity of the RCT in this instance was not compromised by study participant influence networks, RCT designs should consider how influence networks may extend beyond boundaries established by the randomization process in implementation studies. Trial registration NCT00880126 PMID:24229373

An adaptive random search for short term generation scheduling with network constraints.

PubMed

Marmolejo, J A; Velasco, Jonás; Selley, Héctor J

2017-01-01

This paper presents an adaptive random search approach to address a short term generation scheduling with network constraints, which determines the startup and shutdown schedules of thermal units over a given planning horizon. In this model, we consider the transmission network through capacity limits and line losses. The mathematical model is stated in the form of a Mixed Integer Non Linear Problem with binary variables. The proposed heuristic is a population-based method that generates a set of new potential solutions via a random search strategy. The random search is based on the Markov Chain Monte Carlo method. The main key of the proposed method is that the noise level of the random search is adaptively controlled in order to exploring and exploiting the entire search space. In order to improve the solutions, we consider coupling a local search into random search process. Several test systems are presented to evaluate the performance of the proposed heuristic. We use a commercial optimizer to compare the quality of the solutions provided by the proposed method. The solution of the proposed algorithm showed a significant reduction in computational effort with respect to the full-scale outer approximation commercial solver. Numerical results show the potential and robustness of our approach.

Adaptive mechanism-based congestion control for networked systems

NASA Astrophysics Data System (ADS)

Liu, Zhi; Zhang, Yun; Chen, C. L. Philip

2013-03-01

In order to assure the communication quality in network systems with heavy traffic and limited bandwidth, a new ATRED (adaptive thresholds random early detection) congestion control algorithm is proposed for the congestion avoidance and resource management of network systems. Different to the traditional AQM (active queue management) algorithms, the control parameters of ATRED are not configured statically, but dynamically adjusted by the adaptive mechanism. By integrating with the adaptive strategy, ATRED alleviates the tuning difficulty of RED (random early detection) and shows a better control on the queue management, and achieve a more robust performance than RED under varying network conditions. Furthermore, a dynamic transmission control protocol-AQM control system using ATRED controller is introduced for the systematic analysis. It is proved that the stability of the network system can be guaranteed when the adaptive mechanism is finely designed. Simulation studies show the proposed ATRED algorithm achieves a good performance in varying network environments, which is superior to the RED and Gentle-RED algorithm, and providing more reliable service under varying network conditions.

Spike-timing-dependent plasticity enhanced synchronization transitions induced by autapses in adaptive Newman-Watts neuronal networks.

PubMed

Gong, Yubing; Wang, Baoying; Xie, Huijuan

2016-12-01

In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on synchronization transitions induced by autaptic activity in adaptive Newman-Watts Hodgkin-Huxley neuron networks. It is found that synchronization transitions induced by autaptic delay vary with the adjusting rate A p of STDP and become strongest at a certain A p value, and the A p value increases when network randomness or network size increases. It is also found that the synchronization transitions induced by autaptic delay become strongest at a certain network randomness and network size, and the values increase and related synchronization transitions are enhanced when A p increases. These results show that there is optimal STDP that can enhance the synchronization transitions induced by autaptic delay in the adaptive neuronal networks. These findings provide a new insight into the roles of STDP and autapses for the information transmission in neural systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Effect of inhibitory firing pattern on coherence resonance in random neural networks

NASA Astrophysics Data System (ADS)

Yu, Haitao; Zhang, Lianghao; Guo, Xinmeng; Wang, Jiang; Cao, Yibin; Liu, Jing

2018-01-01

The effect of inhibitory firing patterns on coherence resonance (CR) in random neuronal network is systematically studied. Spiking and bursting are two main types of firing pattern considered in this work. Numerical results show that, irrespective of the inhibitory firing patterns, the regularity of network is maximized by an optimal intensity of external noise, indicating the occurrence of coherence resonance. Moreover, the firing pattern of inhibitory neuron indeed has a significant influence on coherence resonance, but the efficacy is determined by network property. In the network with strong coupling strength but weak inhibition, bursting neurons largely increase the amplitude of resonance, while they can decrease the noise intensity that induced coherence resonance within the neural system of strong inhibition. Different temporal windows of inhibition induced by different inhibitory neurons may account for the above observations. The network structure also plays a constructive role in the coherence resonance. There exists an optimal network topology to maximize the regularity of the neural systems.

Encoding dependence in Bayesian causal networks

USDA-ARS?s Scientific Manuscript database

Bayesian networks (BNs) represent complex, uncertain spatio-temporal dynamics by propagation of conditional probabilities between identifiable states with a testable causal interaction model. Typically, they assume random variables are discrete in time and space with a static network structure that ...

Optimization of protease-inhibitor interactions by randomizing adventitious contacts

PubMed Central

Komiyama, Tomoko; VanderLugt, Bryan; Fugère, Martin; Day, Robert; Kaufman, Randal J.; Fuller, Robert S.

2003-01-01

Polypeptide protease inhibitors are often found to inhibit targets with which they did not coevolve, as in the case of high-affinity inhibition of bacterial subtilisin by the leech inhibitor eglin c. Two kinds of contacts exist in such complexes: (i) reactive site loop-active site contacts and (ii) interactions outside of these that form the broader enzyme-inhibitor interface. We hypothesized that the second class of “adventitious” contacts could be optimized to generate significant increases in affinity for a target enzyme or discrimination of an inhibitor for closely related target proteases. We began with a modified eglin c, Arg-42–Arg-45–eglin, in which the reactive site loop had been optimized for subtilisin-related processing proteases of the Kex2/furin family. We randomized 10 potential adventitious contact residues and screened for inhibition of soluble human furin. Substitutions at one of these sites, Y49, were also screened against yeast Kex2 and human PC7. These screens identified not only variants that exhibited increased affinity (up to 20-fold), but also species that exhibited enhanced selectivity, that is, increased discrimination between the target enzymes (up to 41-fold for furin versus PC7 and 20-fold for PC7 versus furin). One variant, Asp-49–Arg-42–Arg-45–eglin, exhibited a Ki of 310 pM for furin and blocked furin-dependent processing of von Willebrand factor in COS-1 cells when added to the culture medium of the cells. The exploitation of adventitious contact sites may provide a versatile technique for developing potent, selective inhibitors for newly discovered proteases and could in principle be applied to optimize numerous protein–protein interactions. PMID:12832612

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

NASA Astrophysics Data System (ADS)

Lu, Qiming

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

SEQUOIA: significance enhanced network querying through context-sensitive random walk and minimization of network conductance.

PubMed

Jeong, Hyundoo; Yoon, Byung-Jun

2017-03-14

Network querying algorithms provide computational means to identify conserved network modules in large-scale biological networks that are similar to known functional modules, such as pathways or molecular complexes. Two main challenges for network querying algorithms are the high computational complexity of detecting potential isomorphism between the query and the target graphs and ensuring the biological significance of the query results. In this paper, we propose SEQUOIA, a novel network querying algorithm that effectively addresses these issues by utilizing a context-sensitive random walk (CSRW) model for network comparison and minimizing the network conductance of potential matches in the target network. The CSRW model, inspired by the pair hidden Markov model (pair-HMM) that has been widely used for sequence comparison and alignment, can accurately assess the node-to-node correspondence between different graphs by accounting for node insertions and deletions. The proposed algorithm identifies high-scoring network regions based on the CSRW scores, which are subsequently extended by maximally reducing the network conductance of the identified subnetworks. Performance assessment based on real PPI networks and known molecular complexes show that SEQUOIA outperforms existing methods and clearly enhances the biological significance of the query results. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/SEQUOIA .