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News about social simulation, social networks dynamics and complex systems
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How People Interact in Evolving Online Affiliation Networks

How People Interact in Evolving Online Affiliation Networks | Social Simulation | Scoop.it

The concept of social networks, in the age of Twitter and Facebook, seems like a really banal one. Social networks, however, have turned out to be a fertile ground for scientific studies of human interactions by not only social scientists, but also by physicists, from which we gain illuminating insights about ourselves and our societies. For example, why, and how, do we make new friends or establish fresh social ties? In this paper, we show that meaningful answers to these questions can be learned, by bringing concepts and methods from statistical physics to bear in a new analysis of the detailed growth dynamics of two networks associated with two online social-networking sites.

 

How People Interact in Evolving Online Affiliation Networks

Lazaros K. Gallos, Diego Rybski, Fredrik Liljeros, Shlomo Havlin, and Hernán A. Makse


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Urban world: Cities and the rise of the consuming class

Urban world: Cities and the rise of the consuming class | Social Simulation | Scoop.it

Cities have long been the world’s economic dynamos, but today the speed and scale of their expansion are unprecedented. Through a combination of consumption and investment in physical capital, growing cities could inject up to $30 trillion a year into the world economy by 2025. Understanding cities and their shifting demographics is critical to reaching urban consumers and to preparing for the challenges that will arise from increasing demand for natural resources (such as water and energy) and for capital to invest in new housing, office buildings, and port capacity.

 

Report|McKinsey Global Institute
Urban world: Cities and the rise of the consuming class
June 2012 | by Richard Dobbs, Jaana Remes, James Manyika, Charles Roxburgh, Sven Smit and Fabian Schaer


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Dynamic Network Centrality Summarizes Learning in the Human Brain

Alexander V. Mantzaris, Danielle S. Bassett, Nicholas F. Wymbs, Ernesto Estrada, Mason A. Porter, Peter J. Mucha, Scott T. Grafton, Desmond J. Higham
(Submitted on 20 Jul 2012)
We study functional activity in the human brain using functional Magnetic Resonance Imaging and recently developed tools from network science. The data arise from the performance of a simple behavioural motor learning task. Unsupervised clustering of subjects with respect to similarity of network activity measured over three days of practice produces significant evidence of 'learning', in the sense that subjects typically move between clusters (of subjects whose dynamics are similar) as time progresses. However, the high dimensionality and time-dependent nature of the data makes it difficult to explain which brain regions are driving this distinction. Using network centrality measures that respect the arrow of time, we express the data in an extremely compact form that characterizes the aggregate activity of each brain region in each experiment using a single coefficient, while reproducing information about learning that was discovered using the full data set. This compact summary allows key brain regions contributing to centrality to be visualized and interpreted. We thereby provide a proof of principle for the use of recently proposed dynamic centrality measures on temporal network data in neuroscience.

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Rescooped by Frédéric Amblard from Network Science
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Long-Range Navigation on Complex Networks using Lévy Random Walks

We introduce a new strategy of navigation in undirected networks, including regular, random and complex networks, that is inspired by L\'evy random walks, generalizing previous navigation rules. We obtained exact expressions for the stationary probability distribution, the occupation probability, the mean first passage time and the average time to reach a node on the network. We found that the long-range navigation using the L\'evy random walk strategy, in comparison with the normal random walk strategy, is more efficient to reduce the time to cover the network. The dynamical effect of using the L\'evy walk strategy is to transform a large-world network into a small world. Our exact results provide a general framework that connects two important fields: L\'evy navigation strategies and dynamics in complex


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Activity driven modeling of time varying networks : Scientific Reports : Nature Publishing Group

Activity driven modeling of time varying networks : Scientific Reports : Nature Publishing Group | Social Simulation | Scoop.it

Network modeling plays a critical role in identifying statistical regularities and structural principles common to many systems. The large majority of recent modeling approaches are connectivity driven. The structural patterns of the network are at the basis of the mechanisms ruling the network formation. Connectivity driven models necessarily provide a time-aggregated representation that may fail to describe the instantaneous and fluctuating dynamics of many networks. We address this challenge by defining the activity potential, a time invariant function characterizing the agents' interactions and constructing an activity driven model capable of encoding the instantaneous time description of the network dynamics. The model provides an explanation of structural features such as the presence of hubs, which simply originate from the heterogeneous activity of agents. Within this framework, highly dynamical networks can be described analytically, allowing a quantitative discussion of the biases induced by the time-aggregated representations in the analysis of dynamical processes.

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A Tunable Mechanism for Identifying Trusted Nodes in Large Scale Distributed Networks

In this paper, we propose a simple randomized protocol for identifying trusted nodes based on personalized trust in large scale distributed networks. The problem of identifying trusted nodes, based on personalized trust, in a large network setting stems from the huge computation and message overhead involved in exhaustively calculating and propagating the trust estimates by the remote nodes. However, in any practical scenario, nodes generally communicate with a small subset of nodes and thus exhaustively estimating the trust of all the nodes can lead to huge resource consumption. In contrast, our mechanism can be tuned to locate a desired subset of trusted nodes, based on the allowable overhead, with respect to a particular user. The mechanism is based on a simple exchange of random walk messages and nodes counting the number of times they are being hit by random walkers of nodes in their neighborhood. Simulation results to analyze the effectiveness of the algorithm show that using the proposed algorithm, nodes identify the top trusted nodes in the network with a very high probability by exploring only around 45% of the total nodes, and in turn generates nearly 90% less overhead as compared to an exhaustive trust estimation mechanism, named TrustWebRank. Finally, we provide a measure of the global trustworthiness of a node; simulation results indicate that the measures generated using our mechanism differ by only around 0.6% as compared to TrustWebRank.

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Predicting the behavior of interacting humans by fusing data from multiple sources

Multi-fidelity methods combine inexpensive low-fidelity simulations with costly but high-fidelity simulations to produce an accurate model of a system of interest at minimal cost. They have proven useful in modeling physical systems and have been applied to engineering problems such as wing-design optimization. During human-in-the-loop experimentation, it has become increasingly common to use online platforms, like Mechanical Turk, to run low-fidelity experiments to gather human performance data in an efficient manner. One concern with these experiments is that the results obtained from the online environment generalize poorly to the actual domain of interest. To address this limitation, we extend traditional multi-fidelity approaches to allow us to combine fewer data points from high-fidelity human-in-the-loop experiments with plentiful but less accurate data from low-fidelity experiments to produce accurate models of how humans interact. We present both model-based and model-free methods, and summarize the predictive performance of each method under different conditions.

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Rescooped by Frédéric Amblard from Papers
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Social Dynamics of Digg

Online social media provide multiple ways to find interesting content. One important method is highlighting content recommended by user's friends. We examine this process on one such site, the news aggregator Digg. With a stochastic model of user behavior, we distinguish the effects of the content visibility and interestingness to users. We find a wide range of interest and distinguish stories primarily of interest to a users' friends from those of interest to the entire user community. We show how this model predicts a story's eventual popularity from users' early reactions to it, and estimate the prediction reliability. This modeling framework can help evaluate alternative design choices for displaying content on the site.

 

Social Dynamics of Digg
Tad Hogg and Kristina Lerman

EPJ Data Science 2012, 1:5 http://dx.doi.org/10.1140/epjds5


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Rescooped by Frédéric Amblard from Social Foraging
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Listen to Music That Evolved From Random Noise

Listen to Music That Evolved From Random Noise | Social Simulation | Scoop.it

Music, you might think, is an innately human pursuit; something which can only truly be written by us, the most intelligent of creatures. But a team of researchers has demonstratedthat, actually, it's possible for digital music to evolve by itself, without any creative input from a composer.Armand Leroi, professor of evolutionary developmental biology at Imperial College London, explains the thinking behind the work to the BBC:

 

"We don't often think of music as evolving, but everybody knows it has a history and it has traditions. But if you think about it, it really has evolved, it is changing continuously. We believe music evolves by a fundamentally Darwinian process - so we wanted to test that idea."


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Understanding mobility in a social petri dish : Scientific Reports : Nature Publishing Group

Understanding mobility in a social petri dish : Scientific Reports : Nature Publishing Group | Social Simulation | Scoop.it

Despite the recent availability of large data sets on human movements, a full understanding of the rules governing motion within social systems is still missing, due to incomplete information on the socio-economic factors and to often limited spatio-temporal resolutions. Here we study an entire society of individuals, the players of an online-game, with complete information on their movements in a network-shaped universe and on their social and economic interactions. Such a “socio-economic laboratory” allows to unveil the intricate interplay of spatial constraints, social and economic factors, and patterns of mobility. We find that the motion of individuals is not only constrained by physical distances, but also strongly shaped by the presence of socio-economic areas. These regions can be recovered perfectly by community detection methods solely based on the measured human dynamics. Moreover, we uncover that long-term memory in the time-order of visited locations is the essential ingredient for modeling the trajectories.

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[1206.2369] Networks in Motion

Adilson E. Motter, Reka Albert
(Submitted on 11 Jun 2012)
Feature article on how networks that govern communication, growth, herd behavior, and other key processes in nature and society are becoming increasingly amenable to modeling, forecast, and control.

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Rescooped by Frédéric Amblard from Complex Systems
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IU community mourns passing of Distinguished Professor and Nobel Laureate Elinor Ostrom: IU News Room: Indiana University

IU community mourns passing of Distinguished Professor and Nobel Laureate Elinor Ostrom: IU News Room: Indiana University | Social Simulation | Scoop.it
The entire Indiana University community mourns the passing of Distinguished Professor Elinor Ostrom, who received the 2009 Nobel Prize in Economic Sciences for her groundbreaking research on the ways that people organize themselves to manage...

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Rescooped by Frédéric Amblard from Talks
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How We Came To Be Deluged By Tweets

by James Gleick
May 16, 2012

 

The story of information began in a time profoundly unlike our own, when every thought and utterance vanishes as soon as it is born. From the invention of scripts and alphabets to the long-misunderstood talking drums of Africa, Gleick tells the story of information technologies that changed the very nature of human consciousness. He provides portraits of the key figures contributing to the inexorable development of our modern understanding of information: Charles Babbage, Ada Byron, Samuel Morse, Alan Turing and Claude Shannon, the creator of information theory itself.


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Tracking Down an Epidemic’s Source

Tracking Down an Epidemic’s Source | Social Simulation | Scoop.it
Researchers find the source of an epidemic using relatively little information. Their technique could also help authorities track down contamination in water systems or locate problems in electrical grids.
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Opinions, Conflicts and Consensus: Modeling Social Dynamics in a Collaborative Environment

János Török, Gerardo Iñiguez, Taha Yasseri, Maxi San Miguel, Kimmo Kaski, János Kertész
(Submitted on 20 Jul 2012)
Information-communication technology promotes collaborative environments like Wikipedia where, however, controversiality and conflicts can appear. To describe the rise, persistence, and resolution of such conflicts we devise an extended opinion dynamics model where agents with different opinions perform a single task to make a consensual product. As a function of the convergence parameter describing the influence of the product on the agents, the model shows spontaneous symmetry breaking of the final consensus opinion represented by the medium. For the case when agents are replaced with new ones at a certain rate, a transition from mainly consensus to a perpetual conflict occurs, which is in qualitative agreement with the scenarios observed in Wikipedia.

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Peer-to-Peer and Mass Communication Effect on Revolution Dynamics

Alex Kindler, Sorin Solomon, Dietrich Stauffer
(Submitted on 22 Jul 2012)
Revolution dynamics is studied through a minimal Ising model with three main influences (fields): personal conservatism (power-law distributed), inter-personal and group pressure, and a global field incorporating peer-to-peer and mass communications, which is generated bottom-up from the revolutionary faction. A rich phase diagram appears separating possible terminal stages of the revolution, characterizing failure phases by the features of the individuals who had joined the revolution. An exhaustive solution of the model is produced, allowing predictions to be made on the revolution's outcome.

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Identifying Influential and Susceptible Members of Social Networks

Identifying social influence in networks is critical to understanding how behaviors spread. We present a method for identifying influence and susceptibility in networks that avoids biases in traditional estimates of social contagion by leveraging in vivo randomized experimentation. Estimation in a representative sample of 1.3 million Facebook users showed that younger users are more susceptible than older users, men are more influential than women, women influence men more than they influence other women, and married individuals are the least susceptible to influence in the decision to adopt the product we studied. Analysis of influence and susceptibility together with network structure reveals that influential individuals are less susceptible to influence than non-influential individuals and that they cluster in the network, which suggests that influential people with influential friends help spread this product.

 

Identifying Influential and Susceptible Members of Social Networks
Sinan Aral, Dylan Walker

Science http://dx.doi.org/10.1126/science.1215842


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Complex networks embedded in space: Dimension and scaling relations between mass, topological distance and Euclidean distance

Many real networks are embedded in space, where in some of them the links length decay as a power law distribution with distance. Indications that such systems can be characterized by the concept of dimension were found recently. Here, we present further support for this claim, based on extensive numerical simulations for model networks embedded on lattices of dimensions $d_e=1$ and $d_e=2$.
We evaluate the dimension $d$ from the power law scaling of (a) the mass of the network with the Euclidean radius $r$ and (b) the probability of return to the origin with the distance $r$ travelled by the random walker. Both approaches yield the same dimension. For networks with $\delta < d_e$, $d$ is infinity, while for $\delta > 2d_e$, $d$ obtains the value of the embedding dimension $d_e$. In the intermediate regime of interest $d_e \leq \delta < 2 d_e$, our numerical results suggest that $d$ decreases continously from $d = \infty$ to $d_e$, with $d - d_e \sim (\delta - d_e)^{-1}$ for $\delta$ close to $d_e$. Finally, we discuss the scaling of the mass $M$ and the Euclidean distance $r$ with the topological distance $\ell$. Our results suggest that in the intermediate regime $d_e \leq \delta < 2 d_e$, $M(\ell)$ and $r(\ell)$ do not increase with $\ell$ as a power law but with a stretched exponential, $M(\ell) \sim \exp [A \ell^{\delta' (2 - \delta')}]$ and $r(\ell) \sim \exp [B \ell^{\delta' (2 - \delta')}]$, where $\delta' = \delta/d_e$. The parameters $A$ and $B$ are related to $d$ by $d = A/B$, such that $M(\ell) \sim r(\ell)^d$. For $\delta < d_e$, $M$ increases exponentially with $\ell$, as known for $\delta=0$, while $r$ is constant and independent of $\ell$. For $\delta \geq 2d_e$, we find power law scaling, $M(\ell) \sim \ell^{d_\ell}$ and $r(\ell) \sim \ell^{1/d_{min}}$, with $d_\ell \cdot d_{min} = d$.

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Temporal Heterogeneities Increase the Prevalence of Epidemics on Evolving Networks

Empirical studies suggest that contact patterns follow heterogeneous inter-event times, meaning that intervals of high activity are followed by periods of inactivity. Combined with birth and death of individuals, these temporal constraints affect the spread of infections in a non-trivial way and are dependent on the particular contact dynamics. We propose a stochastic model to generate temporal networks where vertices make instantaneous contacts following heterogeneous inter-event times, and leave and enter the system at fixed rates. We study how these temporal properties affect the prevalence of an infection and estimate R0, the number of secondary infections, by modeling simulated infections (SIR, SI and SIS) co-evolving with the network structure. We find that heterogeneous contact patterns cause earlier and larger epidemics on the SIR model in comparison to homogeneous scenarios. In case of SI and SIS, the epidemics is faster in the early stages (up to 90% of prevalence) followed by a slowdown in the asymptotic limit in case of heterogeneous patterns. In the presence of birth and death, heterogeneous patterns always cause higher prevalence in comparison to homogeneous scenarios with same average inter-event times. Our results suggest that R0 may be underestimated if temporal heterogeneities are not taken into account in the modeling of epidemics.

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Using large-scale brain simulations for machine learning and A.I.

You probably use machine learning technology dozens of times a day without knowing it—it’s a way of training computers on real-world data, and it enables high-quality speech recognition, practical computer vision, email spam blocking and even self-driving cars. But it’s far from perfect—you’ve probably chuckled at poorly transcribed text, a bad translation or a misidentified image. We believe machine learning could be far more accurate, and that smarter computers could make everyday tasks much easier. So our research team has been working on some new approaches to large-scale machine learning.

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Rescooped by Frédéric Amblard from Augmented Collective Intelligence
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Mob rule: Iceland crowdsources its next constitution

Mob rule: Iceland crowdsources its next constitution | Social Simulation | Scoop.it

Public sphere meets collective intelligence. -- Howard

 

"It is not the way the scribes of yore would have done it but Iceland is tearing up the rulebook by drawing up its new constitution through crowdsourcing.

As the country recovers from the financial crisis that saw the collapse of its banks and government, it is using social media to get its citizens to share their ideas as to what the new document should contain.

"I believe this is the first time a constitution is being drafted basically on the internet," said Thorvaldur Gylfason, member of Iceland's constitutional council."


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Understanding mobility in a social petri dish : Scientific Reports : Nature Publishing Group

Understanding mobility in a social petri dish : Scientific Reports : Nature Publishing Group | Social Simulation | Scoop.it

Despite the recent availability of large data sets on human movements, a full understanding of the rules governing motion within social systems is still missing, due to incomplete information on the socio-economic factors and to often limited spatio-temporal resolutions. Here we study an entire society of individuals, the players of an online-game, with complete information on their movements in a network-shaped universe and on their social and economic interactions. Such a “socio-economic laboratory” allows to unveil the intricate interplay of spatial constraints, social and economic factors, and patterns of mobility. We find that the motion of individuals is not only constrained by physical distances, but also strongly shaped by the presence of socio-economic areas. These regions can be recovered perfectly by community detection methods solely based on the measured human dynamics. Moreover, we uncover that long-term memory in the time-order of visited locations is the essential ingredient for modeling the trajectories.

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Temporal percolation of the susceptible network in an epidemic spreading

L. D. Valdez, P. A. Macri, L. A. Braunstein
(Submitted on 13 Jun 2012)
In this work, we study the evolution of the susceptible individuals during the spread of an epidemic modeled by the susceptible-infected-recovered (SIR) process spreading on top of complex networks. Using a edge-based compartmental approach and percolation tools, we find that the network composed by susceptible individuals undergoes a dynamic node percolation transition with a time dependent control parameter. We show that there exists a critical time $t_c$ above which the giant susceptible cluster is destroyed. As a consequence, in order to protect a large fraction of the susceptible individuals, any mitigation strategy should be implemented before this critical time $t_c$. Our theoretical results are confirmed by extensive simulations of the SIR process.

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[1206.2858] Walking and searching in time-varying networks

Nicola Perra, Andrea Baronchelli, Delia Mocanu, Bruno Gonçalves, Romualdo Pastor-Satorras, Alessandro Vespignani
(Submitted on 13 Jun 2012)
The random walk process lies underneath the description of a large number or real world phenomena. Here we provide a general framework for the study of random walk processes in time varying networks in the regime of time-scale mixing; i.e. when the network connectivity pattern and the random walk process dynamics are unfolding on the same time scale. We consider a model for time varying networks created from the activity potential of the nodes, and derive solutions of the asymptotic behavior of random walks and the mean first passage time in undirected and directed networks. Our findings show striking differences with respect to the well known results obtained in quenched and annealed networks, emphasizing the effects of dynamical connectivity patterns in the definition of proper strategies for search, retrieval and diffusion processes in time-varying networks.

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Rescooped by Frédéric Amblard from Center for Collective Dynamics of Complex Systems (CoCo)
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NetSci High 2011 / 2012

NetSci High 2011 / 2012 | Social Simulation | Scoop.it

NetSci High: High School Student Research on Network Science is a pilot educational outreach program organized and run by the community of network scientists. It aims to infuse Network Science, an emerging interdisciplinary field of study on complex networks, into K-12 education by connecting high school students and teachers to university research labs and letting them work on current network science research for several months, culminating in a poster presentation at NetSci conferences. This program is financially supported by the US National Science Foundation*, as well as a kind corporate donation from BAE Systems.


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