Your new post is loading...
Your new post is loading...

Scooped by
Shaolin Tan

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 twostrategy 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 snowdrift 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.

Scooped by
Shaolin Tan

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 networkwide 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 highthroughput strategy to develop comprehensive metrics for cybersecurity.

Scooped by
Shaolin Tan

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.

Scooped by
Shaolin Tan

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 birthdeath process and suppress the selection effect on the deathbirth process. Based on the above results, an effective algorithm is proposed to generate selection adjusters with desired size and average degree.

Scooped by
Shaolin Tan

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 thatfor the SIS modeldifferential 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.

Rescooped by
Shaolin Tan
from Papers

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 oneyear 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 ongoing news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for ongoing 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 ;
Via Complexity Digest

Scooped by
Shaolin Tan

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 userinterface 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.

Rescooped by
Shaolin Tan
from Papers

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, datadriven 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 longterm 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
Via Complexity Digest
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

Rescooped by
Shaolin Tan
from Papers

Collective, especially groupbased, managerial decision making is crucial in organizations. Using an evolutionary theory approach to collective decision making, agentbased 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 grouplevel bias in collective problem understanding. Simulation results also indicated the importance of balance between selectionoriented (i.e., exploitative) and variationoriented (i.e., explorative) behaviors in discussion to achieve quality final decisions. Expanding the group size and introducing nontrivial 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 smallworld networks with high local clustering tended to achieve highest decision quality more often than on random or scalefree networks. Implications of this evolutionary theory and simulation approach for future managerial research on collective, group, and multilevel decision making are discussed. Evolutionary perspectives on collective decision making: Studying the implications of diversity and social network structure with agentbased simulations Hiroki Sayama, Shelley D. Dionne, Francis J. Yammarino http://arxiv.org/abs/1311.3674
Via Complexity Digest
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

Rescooped by
Shaolin Tan
from Papers

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 selfconsistent 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 steadystate 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 networkbased 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 & AlbertLászló Barabási Nature Physics 9, 673–681 (2013) http://dx.doi.org/10.1038/nphys2741
Via Complexity Digest

Scooped by
Shaolin Tan

Recent empirical research has shown that links between groups reinforce individuals within groups to adopt cooperative behaviour.

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 birthdeath process and suppress the selection effect on the deathbirth process. Based on the above results, an effective algorithm is proposed to generate selection adjusters with desired size and average degree.
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.

Scooped by
Shaolin Tan

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 minimumcost condition, we introduce a novel directionspecific 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.

Scooped by
Shaolin Tan

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 WattsStrogatz networks a broad pronounced bimodal distribution for the loads. To identify the most vulnerable nodes, we introduce the concept of nodebasin size, a purely topological property which we show to be strongly correlated to the average load of a node.

Scooped by
Shaolin Tan

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 graphtheoretic 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.

Rescooped by
Shaolin Tan
from Papers

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
Via Complexity Digest

Scooped by
Shaolin Tan


Scooped by
Shaolin Tan

Networks that fail can sometimes recover spontaneously[mdash]think of traffic jams suddenly easing or people waking from a coma.

Scooped by
Shaolin Tan

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.

Scooped by
Shaolin Tan


Scooped by
Shaolin Tan

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.
