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 Rescooped by Roger D. Jones, PhD from Non-Equilibrium Social Science onto Complex Systems and X-Events

# Topics in social network analysis and network science

This chapter introduces statistical methods used in the analysis of social networks and in the rapidly evolving parallel-field of network science. Although several instances of social network analysis in health services research have appeared recently, the majority involve only the most basic methods and thus scratch the surface of what might be accomplished. Cutting-edge methods using relevant examples and illustrations in health services research are provided.

by A. James O'Malley, Jukka-Pekka Onnela

arXiv:1404.0067 [physics.soc-ph]

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Eli Levine's curator insight,

A very cool and comprehensive look at how networks can be analyzed, studied and examined.

Way cool science!

# Complex Systems and X-Events

 Rescooped by Roger D. Jones, PhD from Complex World

## Hidden scaling patterns and universality in written communication

The temporal statistics exhibited by written correspondence appear to be media dependent, with features which have so far proven difficult to characterize. We explain the origin of these difficulties by disentangling the role of spontaneous activity from decision-based prioritizing processes in human dynamics, clocking all waiting times through each agent's proper time'' measured by activity. This unveils the same fundamental patterns in written communication across all media (letters, email, sms), with response times displaying truncated power-law behavior and average exponents near -${}\frac{3}{2}$. When standard time is used, the response time probabilities are theoretically predicted to exhibit a bimodal character, which is empirically borne out by our newly collected years-long data on email. These perspectives on the temporal dynamics of human correspondence should aid in the analysis of interaction phenomena in general, including resource management, optimal pricing and routing, information sharing, and emergency handling.

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## A Brazilian Wunderkind Who Calms Chaos

Artur Avila’s solutions to ubiquitous problems in chaos theory have “changed the face of the field,” earning him Brazil’s first Fields Medal.

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## Scaling of Chaos versus Periodicity: How Certain is it that an Attractor is Chaotic?

A small perturbation in a system's parameter can convert its attractor from chaotic to periodic, where the probability of obtaining a chaotic regime scales as a power law with respect to the perturbation size.

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## Percolation and cooperation with mobile agents: Geometric and strategy clusters

We study the conditions for persistent cooperation in an off-lattice model of mobile agents playing the Prisoner's Dilemma game with pure, unconditional strategies. Each agent has an exclusion radius ${r}_{P}$, which accounts for the population viscosity, and an interaction radius ${r}_{\mathrm{int}}$, which defines the instantaneous contact network for the game dynamics. We show that, differently from the ${r}_{P}=0$ case, the model with finite-sized agents presents a coexistence phase with both cooperators and defectors, besides the two absorbing phases, in which either cooperators or defectors dominate. We provide, in addition, a geometric interpretation of the transitions between phases. In analogy with lattice models, the geometric percolation of the contact network (i.e., irrespective of the strategy) enhances cooperation. More importantly, we show that the percolation of defectors is an essential condition for their survival. Differently from compact clusters of cooperators, isolated groups of defectors will eventually become extinct if not percolating, independently of their size.

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 Rescooped by Roger D. Jones, PhD from Dynamics on complex networks

## The Simple Rules of Social Contagion

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 significantly more complex than the prediction of the pathogen model. 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 the 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 apply our model to real-time forecasting of user behavior.

The Simple Rules of Social Contagion
Nathan O. Hodas, Kristina Lerman

http://arxiv.org/abs/1308.5015

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António F Fonseca's curator insight,

Another paper about information propagation. A study on the user interface of two social sites, mainly the problem of limited attention and attention managment.

 Rescooped by Roger D. Jones, PhD from Dynamics on complex networks

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

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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## Modelling social networks reveals how information spreads

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.

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Marc Tirel's curator insight,

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.

Shaolin Tan's curator 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.

António F Fonseca's curator insight,

First principles in information difusion on networks.

 Rescooped by Roger D. Jones, PhD from Dynamics on complex networks

## 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

Via Complexity Digest, Shaolin Tan
António F Fonseca's curator insight,

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

Audiref Cía.'s curator insight,

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.

 Rescooped by Roger D. Jones, PhD from Dynamics on complex networks

## 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

Via Complexity Digest, Shaolin Tan
Eli Levine's curator insight,

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....

 Rescooped by Roger D. Jones, PhD from Dynamics on complex networks

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

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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 Rescooped by Roger D. Jones, PhD from Dynamics on complex networks

## Beyond network structure: How heterogeneous susceptibility modulates the spread of epidemics : Scientific Reports : Nature Publishing Group

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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## Connecting Core Percolation and Controllability of Complex Networks : Scientific Reports : Nature Publishing Group

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,

 Rescooped by Roger D. Jones, PhD from Dynamics on complex networks

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

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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 Rescooped by Roger D. Jones, PhD from Complex World

## Physicists eye neural fly data, find formula for Zipf's law

Physicists have identified a mechanism that may help explain Zipf's law – a unique pattern of behavior found in disparate systems, including complex biological ones. The journal Physical Review Letters is publishing their mathematical models, which demonstrate how Zipf's law naturally arises when a sufficient number of units react to a hidden variable in a system.

Via Claudia Mihai
Jean-Michel Livowsky's curator insight,

Maintenant, on sait pourquoi les terroristes du hamaSS volent, et surtout comment.

 Rescooped by Roger D. Jones, PhD from Complex World

## Only ten midges needed to make a swarm

High-speed cameras reveal when insects become self-organizing.

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## Bayesian Inference of Natural Rankings in Incomplete Competition Networks

Competition between a complex system's constituents and a corresponding reward mechanism based on it have profound influence on the functioning, stability, and evolution of the system. But determining the dominance hierarchy or ranking among the constituent parts from the strongest to the weakest - essential in determining reward and penalty - is frequently an ambiguous task due to the incomplete (partially filled) nature of competition networks. Here we introduce the [ldquo]Natural Ranking,[rdquo] an unambiguous ranking method applicable to a round robin tournament, and formulate an analytical model based on the Bayesian formula for inferring the expected mean and error of the natural ranking of nodes from an incomplete network. We investigate its potential and uses in resolving important issues of ranking by applying it to real-world competition networks.

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## The small-world effect is a modern phenomenon

The "small-world effect" is the observation that one can find a short chain of acquaintances, often of no more than a handful of individuals, connecting almost any two people on the planet. It is often expressed in the language of networks, where it is equivalent to the statement that most pairs of individuals are connected by a short path through the acquaintance network. Although the small-world effect is well-established empirically for contemporary social networks, we argue here that it is a relatively recent phenomenon, arising only in the last few hundred years: for most of mankind's tenure on Earth the social world was large, with most pairs of individuals connected by relatively long chains of acquaintances, if at all. Our conclusions are based on observations about the spread of diseases, which travel over contact networks between individuals and whose dynamics can give us clues to the structure of those networks even when direct network measurements are not available. As an example we consider the spread of the Black Death in 14th-century Europe, which is known to have traveled across the continent in well-defined waves of infection over the course of several years. Using established epidemiological models, we show that such wave-like behavior can occur only if contacts between individuals living far apart are exponentially rare. We further show that if long-distance contacts are exponentially rare, then the shortest chain of contacts between distant individuals is on average a long one. The observation of the wave-like spread of a disease like the Black Death thus implies a network without the small-world effect.

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

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

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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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## Human opinion dynamics: An inspiration to solve complex optimization problems : Scientific Reports : Nature Publishing Group

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.

Via Shaolin Tan
António F Fonseca's curator insight,

Another paper on opinion dynamics.

Luciano Lampi's curator insight,

Humanrithms....

Claude Emond's curator insight,

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

 Rescooped by Roger D. Jones, PhD from Dynamics on complex networks

## The Potential of Social Network Analysis in Intelligence

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, Shaolin Tan
Marc Tirel's curator insight,

huge field for research ...

Catherine Pascal's curator insight,

Très intéressant

 Rescooped by Roger D. Jones, PhD from Dynamics on complex networks

## 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 ;

Via Complexity Digest, Shaolin Tan
Eli Levine's curator insight,

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).

 Rescooped by Roger D. Jones, PhD from Dynamics on complex networks

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

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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## A genetic epidemiology approach to cyber-security : Scientific Reports : Nature Publishing Group

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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