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Control Centrality and Hierarchical Structure in Complex Networks

Control Centrality and Hierarchical Structure in Complex Networks | Dynamics on complex networks | Scoop.it

We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks.


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Dynamics on complex networks
Investigating how agents influence each other through a coupling network
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Local Nash Equilibrium in Social Networks

Local Nash Equilibrium in Social Networks | Dynamics on complex networks | Scoop.it
Nash equilibrium is widely present in various social disputes. As of now, in structured static populations, such as social networks, regular, and random graphs, the discussions on Nash equilibrium are quite limited. In a relatively stable static gaming network, a rational individual has to comprehensively consider all his/her opponents' strategies before they adopt a unified strategy. In this scenario, a new strategy equilibrium emerges in the system. We define this equilibrium as a local Nash equilibrium. In this paper, we present an explicit definition of the local Nash equilibrium for the two-strategy games in structured populations. Based on the definition, we investigate the condition that a system reaches the evolutionary stable state when the individuals play the Prisoner's dilemma and snow-drift game. The local Nash equilibrium provides a way to judge whether a gaming structured population reaches the evolutionary stable state on one hand. On the other hand, it can be used to predict whether cooperators can survive in a system long before the system reaches its evolutionary stable state for the Prisoner's dilemma game. Our work therefore provides a theoretical framework for understanding the evolutionary stable state in the gaming populations with static structures.
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A genetic epidemiology approach to cyber-security : Scientific Reports : Nature Publishing Group

A genetic epidemiology approach to cyber-security : Scientific Reports : Nature Publishing Group | Dynamics on complex networks | Scoop.it
While much attention has been paid to the vulnerability of computer networks to node and link failure, there is limited systematic understanding of the factors that determine the likelihood that a node (computer) is compromised. We therefore collect threat log data in a university network to study the patterns of threat activity for individual hosts. We relate this information to the properties of each host as observed through network-wide scans, establishing associations between the network services a host is running and the kinds of threats to which it is susceptible. We propose a methodology to associate services to threats inspired by the tools used in genetics to identify statistical associations between mutations and diseases. The proposed approach allows us to determine probabilities of infection directly from observation, offering an automated high-throughput strategy to develop comprehensive metrics for cyber-security.
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Connecting Core Percolation and Controllability of Complex Networks : Scientific Reports : Nature Publishing Group

Connecting Core Percolation and Controllability of Complex Networks : Scientific Reports : Nature Publishing Group | Dynamics on complex networks | Scoop.it
Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks.
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Sibout Nooteboom's curator insight, July 13, 3:52 AM

Fascinating advances

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Characterizing the effect of population heterogeneity on evolutionary dynamics on complex networks : Scientific Reports : Nature Publishing Group

Characterizing the effect of population heterogeneity on evolutionary dynamics on complex networks : Scientific Reports : Nature Publishing Group | Dynamics on complex networks | Scoop.it
Recently, the impact of network structure on evolutionary dynamics has been at the center of attention when studying the evolutionary process of structured populations. This paper aims at finding out the key structural feature of network to capture its impact on evolutionary dynamics. To this end, a novel concept called heat heterogeneity is introduced to characterize the structural heterogeneity of network, and the correlation between heat heterogeneity of structure and outcome of evolutionary dynamics is further investigated on various networks. It is found that the heat heterogeneity mainly determines the impact of network structure on evolutionary dynamics on complex networks. In detail, the heat heterogeneity readjusts the selection effect on evolutionary dynamics. Networks with high heat heterogeneity amplify the selection effect on the birth-death process and suppress the selection effect on the death-birth process. Based on the above results, an effective algorithm is proposed to generate selection adjusters with desired size and average degree.
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Beyond network structure: How heterogeneous susceptibility modulates the spread of epidemics : Scientific Reports : Nature Publishing Group

Beyond network structure: How heterogeneous susceptibility modulates the spread of epidemics : Scientific Reports : Nature Publishing Group | Dynamics on complex networks | Scoop.it
The compartmental models used to study epidemic spreading often assume the same susceptibility for all individuals, and are therefore, agnostic about the effects that differences in susceptibility can have on epidemic spreading. Here we show that-for the SIS model-differential susceptibility can make networks more vulnerable to the spread of diseases when the correlation between a node's degree and susceptibility are positive, and less vulnerable when this correlation is negative. Moreover, we show that networks become more likely to contain a pocket of infection when individuals are more likely to connect with others that have similar susceptibility (the network is segregated). These results show that the failure to include differential susceptibility to epidemic models can lead to a systematic over/under estimation of fundamental epidemic parameters when the structure of the networks is not independent from the susceptibility of the nodes or when there are correlations between the susceptibility of connected individuals.
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Uncovering the structure and temporal dynamics of information propagation

Time plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion—when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying network connectivity and transmission rates between nodes are unknown. Inferring the underlying diffusion dynamics is important because it leads to new insights and enables forecasting, as well as influencing or containing information propagation. In this paper we model diffusion as a continuous temporal process occurring at different rates over a latent, unobserved network that may change over time. Given information diffusion data, we infer the edges and dynamics of the underlying network. Our model naturally imposes sparse solutions and requires no parameter tuning. We develop an efficient inference algorithm that uses stochastic convex optimization to compute online estimates of the edges and transmission rates. We evaluate our method by tracking information diffusion among 3.3 million mainstream media sites and blogs, and experiment with more than 179 million different instances of information spreading over the network in a one-year period. We apply our network inference algorithm to the top 5,000 media sites and blogs and report several interesting observations. First, information pathways for general recurrent topics are more stable across time than for on-going news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for on-going news events. Finally, major events, for example, large scale civil unrest as in the Libyan civil war or Syrian uprising, increase the number of information pathways among blogs, and also increase the network centrality of blogs and social media sites.


Uncovering the structure and temporal dynamics of information propagation
MANUEL GOMEZ RODRIGUEZ, JURE LESKOVEC, DAVID BALDUZZI, BERNHARD SCHÖLKOPF
Network Science , Volume 2 , Issue 01 , April 2014, pp 26 - 65
http://dx.doi.org/10.1017/nws.2014.3 ;


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The Simple Rules of Social Contagion : Scientific Reports : Nature Publishing Group

The Simple Rules of Social Contagion : Scientific Reports : Nature Publishing Group | Dynamics on complex networks | Scoop.it
It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is far more complex. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We provide a framework for unifying information visibility, divided attention, and explicit social feedback to predict the temporal dynamics of user behavior.
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The Relative Ineffectiveness of Criminal Network Disruption

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

 

The Relative Ineffectiveness of Criminal Network Disruption
Paul A. C. Duijn, Victor Kashirin & Peter M. A. Sloot

Scientific Reports 4, Article number: 4238 http://dx.doi.org/10.1038/srep04238 ;

 

See also documentary at http://www.youtube.com/watch?v=Qhk9ciHlzzo 


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Eli Levine's curator insight, March 6, 1:34 PM

My only critique of this, is that even by successfully disrupting the social networks, you will ont get rid of the foundations of crime within a society.

 

Greed, lust, violence, all of these things come from the brain and can be seen as mental health problems, rather than necessarily just societal problems.  I think we've got to begin ori sorting th the convected and post conicted crowd, such tht we can understand how their brains work and then, how to help heal them, such that we eliminate criminality and crime inspited lifestyles.  I understand there are dozens of easy ways to be opposed to this and that there are dozes more ways th work (especially here, in america, where we are soc focused on our small "selves" to forget that there is a much much much much larger world out thre, and that of ourselves as well.  We are connected to everyone and everything.  That's science.  To deny that it is otherwise is to invite delusion and hallucinations about reality and to invite other problems into your life and the rest of ours for your deliberate ignorance and unwillingness to escape to where reality simply is unoffensive and not politically motivated other than to help other people.

 

Therefore, let's overcome this monkey need to punish people for crimes they really didn't have much say in (thankst o the primacy of the brain) and start doing some research on these people (even though they should be confined from the rest of the population until treatments and diagnoses have been developed and concluded upon).

 

Think about it.

Rescooped by Shaolin Tan from Propagation Organique, Viralité - Spreading Phenomenons
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The Potential of Social Network Analysis in Intelligence

The Potential of Social Network Analysis in Intelligence | Dynamics on complex networks | Scoop.it
Within its limits, SNA can be applied to identify individuals or organizations within a network, generate new leads and simulate flows of information or money throughout a network.

Via Marc Tirel
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Marc Tirel's curator insight, January 12, 8:44 AM

huge field for research ...

Catherine Pascal's curator insight, February 25, 8:26 AM

 Très intéressant 

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Evolutionary perspectives on collective decision making: Studying the implications of diversity and social network structure with agent-based simulations

Collective, especially group-based, managerial decision making is crucial in organizations. Using an evolutionary theory approach to collective decision making, agent-based simulations were conducted to investigate how collective decision making would be affected by the agents' diversity in problem understanding and/or behavior in discussion, as well as by their social network structure. Simulation results indicated that groups with consistent problem understanding tended to produce higher utility values of ideas and displayed better decision convergence, but only if there was no group-level bias in collective problem understanding. Simulation results also indicated the importance of balance between selection-oriented (i.e., exploitative) and variation-oriented (i.e., explorative) behaviors in discussion to achieve quality final decisions. Expanding the group size and introducing non-trivial social network structure generally improved the quality of ideas at the cost of decision convergence. Simulations with different social network topologies revealed that collective decision making on small-world networks with high local clustering tended to achieve highest decision quality more often than on random or scale-free networks. Implications of this evolutionary theory and simulation approach for future managerial research on collective, group, and multi-level decision making are discussed.

 

Evolutionary perspectives on collective decision making: Studying the implications of diversity and social network structure with agent-based simulations
Hiroki Sayama, Shelley D. Dionne, Francis J. Yammarino

http://arxiv.org/abs/1311.3674


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António F Fonseca's curator insight, December 28, 2013 7:15 AM

Some problems may only be solved through agent-based simulation.

Audiref Cía.'s curator insight, December 28, 2013 11:01 AM

Especialmente el grupo de base, la toma de decisiones colectiva, de gestión es fundamental en las organizaciones. El uso de un enfoque de la teoría evolutiva para la toma de decisiones colectiva, se llevaron a cabo simulaciones basadas en agentes para investigar cómo la toma de decisiones colectiva se vería afectada por la diversidad de los agentes en la comprensión y / o comportamiento en la discusión de problemas, así como por su estructura de red social. Resultados de la simulación indican que los grupos con entendimiento problema constante tienden a producir valores de utilidad más altos de las ideas y muestran una mejor toma de convergencia, pero sólo si no había ningún sesgo a nivel de grupo en la comprensión colectiva problema. 

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Modelling social networks reveals how information spreads

Modelling social networks reveals how information spreads | Dynamics on complex networks | Scoop.it
The way information spreads through society has changed significantly over the past decade with the advent of online social networking.

 

In the context of history, connectors are among the people who are pioneers of social change. We need only look back at political change within global society today to find examples of connectors that played a significant role in affecting that change at the time.

Experiments of this type (perhaps on a larger scale) could help develop our understanding of how our society functions in the 21st century. The speed at which information can spread, and the fidelity of the spread of that information, is important to perhaps all aspects of society. 


Via Marc Tirel
Shaolin Tan's insight:

In the experiment, if individuals are allowed to change to color accroding to the neighborhood, it may be more interesting. I think the evolutionary game dynamics on complex provides a proper model to characterize the above diffusion process.

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Marc Tirel's curator insight, October 14, 2013 4:25 AM

Interesting experimentation and conclusion : 

Experiments of this type (perhaps on a larger scale) could help develop our understanding of how our society functions in the 21st century. The speed at which information can spread, and the fidelity of the spread of that information, is important to perhaps all aspects of society.

António F Fonseca's curator insight, December 28, 2013 7:12 AM

First principles in information difusion on networks.

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Universality in network dynamics

Despite significant advances in characterizing the structural properties of complex networks, a mathematical framework that uncovers the universal properties of the interplay between the topology and the dynamics of complex systems continues to elude us. Here we develop a self-consistent theory of dynamical perturbations in complex systems, allowing us to systematically separate the contribution of the network topology and dynamics. The formalism covers a broad range of steady-state dynamical processes and offers testable predictions regarding the system’s response to perturbations and the development of correlations. It predicts several distinct universality classes whose characteristics can be derived directly from the continuum equation governing the system’s dynamics and which are validated on several canonical network-based dynamical systems, from biochemical dynamics to epidemic spreading. Finally, we collect experimental data pertaining to social and biological systems, demonstrating that we can accurately uncover their universality class even in the absence of an appropriate continuum theory that governs the system’s dynamics.

 

Universality in network dynamics
Baruch Barzel & Albert-László Barabási

Nature Physics 9, 673–681 (2013) http://dx.doi.org/10.1038/nphys2741


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Spreading of cooperative behaviour across interdependent groups : Scientific Reports : Nature Publishing Group

Spreading of cooperative behaviour across interdependent groups : Scientific Reports : Nature Publishing Group | Dynamics on complex networks | Scoop.it
Recent empirical research has shown that links between groups reinforce individuals within groups to adopt cooperative behaviour.
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Characterizing the effect of population heterogeneity on evolutionary dynamics on complex networks : Scientific Reports : Nature Publishing Group

Characterizing the effect of population heterogeneity on evolutionary dynamics on complex networks : Scientific Reports : Nature Publishing Group | Dynamics on complex networks | Scoop.it
Recently, the impact of network structure on evolutionary dynamics has been at the center of attention when studying the evolutionary process of structured populations. This paper aims at finding out the key structural feature of network to capture its impact on evolutionary dynamics. To this end, a novel concept called heat heterogeneity is introduced to characterize the structural heterogeneity of network, and the correlation between heat heterogeneity of structure and outcome of evolutionary dynamics is further investigated on various networks. It is found that the heat heterogeneity mainly determines the impact of network structure on evolutionary dynamics on complex networks. In detail, the heat heterogeneity readjusts the selection effect on evolutionary dynamics. Networks with high heat heterogeneity amplify the selection effect on the birth-death process and suppress the selection effect on the death-birth process. Based on the above results, an effective algorithm is proposed to generate selection adjusters with desired size and average degree.
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Connecting Core Percolation and Controllability of Complex Networks : Scientific Reports : Nature Publishing Group

Connecting Core Percolation and Controllability of Complex Networks : Scientific Reports : Nature Publishing Group | Dynamics on complex networks | Scoop.it
Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks.
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Sibout Nooteboom's curator insight, July 13, 3:52 AM

Fascinating advances

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Controllability and observability analysis for vertex domination centrality in directed networks : Scientific Reports : Nature Publishing Group

Controllability and observability analysis for vertex domination centrality in directed networks : Scientific Reports : Nature Publishing Group | Dynamics on complex networks | Scoop.it
Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importance to quantify a vertex's ability to dominate (control or observe) the state of other vertices. In this paper, based on the determination of controllable and observable subspaces under the global minimum-cost condition, we introduce a novel direction-specific index, domination centrality, to assess the intervention capabilities of vertices in a directed network. Statistical studies demonstrate that the domination centrality is, to a great extent, encoded by the underlying network's degree distribution and that most network positions through which one can intervene in a system are vertices with high domination centrality rather than network hubs. To analyse the interaction and functional dependence between vertices when they are used to dominate a network, we define the domination similarity and detect significant functional modules in glossary and metabolic networks through clustering analysis. The experimental results provide strong evidence that our indices are effective and practical in accurately depicting the structure of directed networks.
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Shock waves on complex networks : Scientific Reports : Nature Publishing Group

Shock waves on complex networks : Scientific Reports : Nature Publishing Group | Dynamics on complex networks | Scoop.it
Power grids, road maps, and river streams are examples of infrastructural networks which are highly vulnerable to external perturbations. An abrupt local change of load (voltage, traffic density, or water level) might propagate in a cascading way and affect a significant fraction of the network. Almost discontinuous perturbations can be modeled by shock waves which can eventually interfere constructively and endanger the normal functionality of the infrastructure. We study their dynamics by solving the Burgers equation under random perturbations on several real and artificial directed graphs. Even for graphs with a narrow distribution of node properties (e.g., degree or betweenness), a steady state is reached exhibiting a heterogeneous load distribution, having a difference of one order of magnitude between the highest and average loads. Unexpectedly we find for the European power grid and for finite Watts-Strogatz networks a broad pronounced bimodal distribution for the loads. To identify the most vulnerable nodes, we introduce the concept of node-basin size, a purely topological property which we show to be strongly correlated to the average load of a node.
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Eli Levine's curator insight, May 20, 8:19 AM

Indeed, this is intuitive enough without the mathematics to back it up.  This could be mapped out and used for prioritizing the defense or attack of various points within the network, either in the digital or analog worlds.

 

Way cool science!

 

Think about it.

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Transittability of complex networks and its applications to regulatory biomolecular networks : Scientific Reports : Nature Publishing Group

Transittability of complex networks and its applications to regulatory biomolecular networks : Scientific Reports : Nature Publishing Group | Dynamics on complex networks | Scoop.it
We have often observed unexpected state transitions of complex systems. We are thus interested in how to steer a complex system from an unexpected state to a desired state. Here we introduce the concept of transittability of complex networks, and derive a new sufficient and necessary condition for state transittability which can be efficiently verified. We define the steering kernel as a minimal set of steering nodes to which control signals must directly be applied for transition between two specific states of a network, and propose a graph-theoretic algorithm to identify the steering kernel of a network for transition between two specific states. We applied our algorithm to 27 real complex networks, finding that sizes of steering kernels required for transittability are much less than those for complete controllability. Furthermore, applications to regulatory biomolecular networks not only validated our method but also identified the steering kernel for their phenotype transitions.
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#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion

#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion | Dynamics on complex networks | Scoop.it

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luiy's curator insight, March 27, 1:44 PM

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

António F Fonseca's curator insight, April 2, 6:01 AM

Another paper about popularity prediction.

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Origin of Peer Influence in Social Networks

Social networks pervade our everyday lives: we interact, influence, and are influenced by our friends and acquaintances. With the advent of the World Wide Web, large amounts of data on social networks have become available, allowing the quantitative analysis of the distribution of information on them, including behavioral traits and fads. Recent studies of correlations among members of a social network, who exhibit the same trait, have shown that individuals influence not only their direct contacts but also friends’ friends, up to a network distance extending beyond their closest peers. Here, we show how such patterns of correlations between peers emerge in networked populations. We use standard models (yet reflecting intrinsically different mechanisms) of information spreading to argue that empirically observed patterns of correlation among peers emerge naturally from a wide range of dynamics, being essentially independent of the type of information, on how it spreads, and even on the class of underlying network that interconnects individuals. Finally, we show that the sparser and clustered the network, the more far reaching the influence of each individual will be.
DOI: http://dx.doi.org/10.1103/PhysRevLett.112.098702

Origin of Peer Influence in Social Networks
Phys. Rev. Lett. 112, 098702 – Published 6 March 2014
Flávio L. Pinheiro, Marta D. Santos, Francisco C. Santos, and Jorge M. Pacheco


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Eli Levine's curator insight, March 10, 5:16 PM

Indeed, we are all interconnected in very profound and subtle ways, whether we accept it or not.


This one's for the Libertarians and conservatives out there, who don't seem to think that their actions effect the other, or that the other can effect them, or that the actions done onto the other will effect the actions that are done onto them by the other.

 

Kind of like how they blame the poor for being angry at the rich, after the poor produced the wealth that engorges the rich.

 

Silly people....

 

Think about it.

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How population heterogeneity in susceptibility and infectivity influences epidemic dynamics

How population heterogeneity in susceptibility and infectivity influences epidemic dynamics | Dynamics on complex networks | Scoop.it
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Spontaneous recovery in dynamical networks : Nature Physics : Nature Publishing Group

Spontaneous recovery in dynamical networks : Nature Physics : Nature Publishing Group | Dynamics on complex networks | Scoop.it
Networks that fail can sometimes recover spontaneously[mdash]think of traffic jams suddenly easing or people waking from a coma.
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Human opinion dynamics: An inspiration to solve complex optimization problems : Scientific Reports : Nature Publishing Group

Human opinion dynamics: An inspiration to solve complex optimization problems : Scientific Reports : Nature Publishing Group | Dynamics on complex networks | Scoop.it
Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics.
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António F Fonseca's curator insight, December 28, 2013 7:14 AM

Another paper on opinion dynamics.

Luciano Lampi's curator insight, January 11, 5:45 PM

Humanrithms....

Claude Emond's curator insight, January 20, 5:51 PM

Opinions are an unescapable part of sharing and influencing the direction of collective intelligence

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Evolution and maintenance of cooperation via inheritance of neighborhood relationship

Evolution and maintenance of cooperation via inheritance of neighborhood relationship | Dynamics on complex networks | Scoop.it
Shaolin Tan's insight:

Cooperative behaviors are ubiquitous in nature and human society. It is very important to understand the internal mechanism of emergence and maintenance of cooperation. As we know now, the offsprings inherit not only the phenotype but also the neighborhood relationship of their parents. Some recent research results show that the interactions among individuals facilitate survival of cooperation through network reciprocity of clustering cooperators. This paper aims at introducing an inheritance mechanism of neighborhood relationship to explore the evolution of cooperation. In detail, a mathematical model is proposed to characterize the evolutionary process with the above inheritance mechanism. Theoretical analysis and numerical simulations indicate that high-level cooperation can emerge and be maintained for a wide variety of cost-to-benefit ratios, even if mutation happens during the evolving process.

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Virality Prediction and Community Structure in Social Networks : Scientific Reports : Nature Publishing Group

Virality Prediction and Community Structure in Social Networks : Scientific Reports : Nature Publishing Group | Dynamics on complex networks | Scoop.it
How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily.
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