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News about social simulation, social networks dynamics and complex systems
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Exploring Statistical and Population Aspects of Network Complexity

The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities.

 

In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples.


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Random walks on temporal networks

Michele Starnini, Andrea Baronchelli, Alain Barrat, and Romualdo Pastor-Satorras

Many natural and artificial networks evolve in time. Nodes and connections appear and disappear at various time scales, and their dynamics has profound consequences for any processes in which they are involved. The first empirical analysis of the temporal patterns characterizing dynamic networks are still recent, so that many questions remain open. Here, we study how random walks, as a paradigm of dynamical processes, unfold on temporally evolving networks. To this aim, we use empirical dynamical networks of contacts between individuals, and characterize the fundamental quantities that impact any general process taking place upon them. Furthermore, we introduce different randomizing strategies that allow us to single out the role of the different properties of the empirical networks. We show that the random walk exploration is slower on temporal networks than it is on the aggregate projected network, even when the time is properly rescaled. In particular, we point out that a fundamental role is played by the temporal correlations between consecutive contacts present in the data. Finally, we address the consequences of the intrinsically limited duration of many real world dynamical networks. Considering the fundamental prototypical role of the random walk process, we believe that these results could help to shed light on the behavior of more complex dynamics on temporally evolving networks.

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Online friendships light up shadow social networks - tech - 18 May 2012 - New Scientist

Online friendships light up shadow social networks - tech - 18 May 2012 - New Scientist | Social Simulation | Scoop.it
The structure of an online social network can be used to deduce connections between people who don't use the service...
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JPMorgan and the price of complexity

JPMorgan and the price of complexity | Social Simulation | Scoop.it

Inevitably, the answer to that question depends on whether you view the financial markets as complicated or complex. If the financial markets are merely complicated, traditional approaches to regulation can be effective: regulators can turn their attention to individual actors within the market and systematically make the requisite changes to restore the market to equilibrium. In a complex system, however, traditional approaches to regulation can be woefully inadequate — small changes may end up having outsized effects, while big changes may end up having little or no effect. In a complex system, you need to focus on the interactions between each of the participants as much as the condition of individual actors.

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La simulation sociale pour combattre la censure

La simulation sociale pour combattre la censure | Social Simulation | Scoop.it

Antonio Casili: "Le samedi 19 mai j’ai été parmi les heureux conférenciers de l’édition 2012 de TEDxParisUniversités. A cette occasion, j’ai pu présenter au public français les résultats du projet ICCU (Internet Censorship and Civil Unrest) que je mène avec Paola Tubaro, enseignante-chercheuse à l’Université de Greenwich, Londres. L’accueil a été plus que chaleureux : la tweeterie m’a porté en triomphe, j’ai reçu les accolades des organisateurs et je me suis imbibé de l’enthousiasme d’étudiants et de militants de tout bord. J’exagère, mais pas tant que ça (suffit de lire le compte-rendu Storify concocté par Gayané Adourian ;). Voici donc le texte et les slides de mon intervention, en attendant la vidéo."

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Emergence and Collapse of Early Villages

Emergence and Collapse of Early Villages | Social Simulation | Scoop.it

Ancestral Pueblo farmers encountered the deep, well watered, and productive soils of the central Mesa Verde region of Southwest Colorado around A.D. 600, and within two centuries built some of the largest villages known up to that time in the U.S. Southwest. But one hundred years later, those villages were empty, and most people had gone. This cycle repeated itself from the mid-A.D. 1000s until 1280, when Puebloan farmers permanently abandoned the entire northern Southwest. Taking an interdisciplinary approach, this book examines how climate change, population size, interpersonal conflict, resource depression, and changing social organization contribute to explaining these dramatic shifts. Comparing the simulations from agent-based models with the precisely dated archaeological record from this area, this text will interest archaeologists working in the Southwest and in Neolithic societies around the world as well as anyone applying modeling techniques to understanding how human societies shape, and are shaped by the environments we inhabit.

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Network Science, a Cambridge University Press Journal

Network Science, a Cambridge University Press Journal | Social Simulation | Scoop.it

Network Science is a new journal for a new discipline -- one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations and other actors within it.

The journal welcomes contributions from researchers in all areas working on network theory, methods and data. With the goal of publishing the first of four annual issues in Spring 2013, the editorial team is currently accepting submissions. Reviews will be double-blind and will be coordinated by the editor for the broad interdisciplinary field to which each member of the editorial team is assigned.

More information can be found on the links to the left, and questions about the submission process can be directed to netsci (at) indiana.edu.

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Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales

Concepts used in the scientific study of complex systems have become so widespread that their use and abuse has led to ambiguity and confusion in their meaning. In this paper we use information theory to provide abstract and concise measures of complexity, emergence, self-organization, and homeostasis. The purpose is to clarify the meaning of these concepts with the aid of the proposed formal measures. In a simplified version of the measures (focussing on the information produced by a system), emergence becomes the opposite of self-organization, while complexity represents their balance. We use computational experiments on random Boolean networks and elementary cellular automata to illustrate our measures at multiple scales.

 

Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales

Carlos Gershenson, Nelson Fernandez

http://arxiv.org/abs/1205.2026


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Ranking and clustering of nodes in networks with smart teleportation

Random teleportation is a necessary evil for ranking and clustering directed networks based on random walks. Teleportation enables ergodic solutions, but the solutions must necessarily depend on the exact implementation and parametrization of the teleportation. For example, in the commonly used PageRank algorithm, the teleportation rate must trade off a heavily biased solution with a uniform solution. Here we show that teleportation to links rather than nodes enables a much smoother trade-off and effectively more robust results. We also show that, by not recording the teleportation steps of the random walker, we can further reduce the effect of teleportation with dramatic effects on clustering.

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The organization of strong links in complex networks

The organization of strong links in complex networks | Social Simulation | Scoop.it

Many complex systems reveal a small-world topology, which allows simultaneously local and global efficiency in the interaction between system constituents. Here, we report the results of a comprehensive study that investigates the relation between the clustering properties in such small-world systems and the strength of interactions between its constituents, quantified by the link weight. For brain, gene, social and language networks, we find a local integrative weight organization in which strong links preferentially occur between nodes with overlapping neighbourhoods; we relate this to global robustness of the clustering to removal of the weakest links. Furthermore, we identify local learning rules that establish integrative networks and improve network traffic in response to past traffic failures. Our findings identify a general organization for complex systems that strikes a balance between efficient local and global communication in their strong interactions, while allowing for robust, exploratory development of weak interactions.

 

The organization of strong links in complex networks

Sinisa Pajevic & Dietmar Plenz

Nature Physics 8, 429–436 (2012) http://dx.doi.org/10.1038/nphys2257


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Targeting the dynamics of complex networks : Scientific Reports : Nature Publishing Group

Targeting the dynamics of complex networks : Scientific Reports : Nature Publishing Group | Social Simulation | Scoop.it

We report on a generic procedure to steer (target) a network's dynamics towards a given, desired evolution. The problem is here tackled through a Master Stability Function approach, assessing the stability of the aimed dynamics, and through a selection of nodes to be targeted. We show that the degree of a node is a crucial element in this selection process, and that the targeting mechanism is most effective in heterogeneous scale-free architectures. This makes the proposed approach applicable to the large majority of natural and man-made networked systems.

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Network Archaeology: Uncovering Ancient Networks from Present-day Interactions

Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.


Via Ashish Umre
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Rescooped by Frédéric Amblard from Global Brain
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Is There Big Money in Big Data?

Is There Big Money in Big Data? | Social Simulation | Scoop.it

Many entrepreneurs foresee vast profits in mining data from online activity and mobile devices. One Wharton business school professor strongly disagrees.


Via Spaceweaver
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HAM – Homotopy Analysis Method to explore non-linear dynamical systems

Most interesting phenomena in physics, social sciences, engineering, and other
disciplines are highly non-linear. This limits the ability to analytically
investigate such systems. Simulations of the dynamical processes are then the tool of
choice to explore the system. However, it is sometimes very important to have a
basic understanding in terms of approximative solutions. Non-linear differential
equations describing the dynamics are known to be harder to solve then
linear ODEs.
One often has to resort to asymptotic techniques or classical perturbation theory to obtain analytical approximations.
Classical perturbation theory strongly depends on small/large physical
parameters. Therefore, such methods are only valid for weakly non-linear systems.

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Google Knowledge Graph: first impressions

Google Knowledge Graph: first impressions | Social Simulation | Scoop.it

The Google’s Knowledge Graph showed up for me this morning — it’s been slowly rolling out since the announcement on Wednesday. It builds lots of research from human language technology (e.g., entity recognition and linking) and the semantic web (graphs of linked data). The slogan, “things not strings”, is brilliant and easily understood.

My first impression is that it’s fast, useful and a great accomplishment but leaves lots of room for improvement and expansion. That last bit is a good thing, at least for those of us in the R&D community. Here are some comments based on some initial experimentation.

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How economists have misunderstood inequality: An interview with James Galbraith

How economists have misunderstood inequality: An interview with James Galbraith | Social Simulation | Scoop.it
An interview with James Galbraith, whose new book, “Inequality and Instability,” attributes the rise in inequality since 1980 to structural changes in the world economy.
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Science/AAAS | Special Issue: Human Conflict

Science/AAAS | Special Issue: Human Conflict | Social Simulation | Scoop.it

In this special issue we consider the deep evolutionary roots of violent confrontation. We trace the trajectory of violence and war throughout history, exploring racism, ethnic conflicts, the rise of terrorism, and the possible future of armed conflicts. We also consider our innate capacity to mediate conflict and our ability to achieve—and live in—peace

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Whales demonstrate humans have no monopoly on altruism

Whales demonstrate humans have no monopoly on altruism | Social Simulation | Scoop.it

Whales prove humans are not the only species capable of disgust at cruelty when they work to defend a baby whale of another species against predators.

 

Human haters abound in the animal rights movement. I can understand their sentiment. It’s very difficult to hear about all of the abuse inflicted upon other species of animals. But in reality some of the most heartbreaking stories I have witnessed have been animal on animal.

 

Perhaps it is more heartbreaking to hear of yet another case of human insensitivity and violence against animals because we expect more out of ourselves. A lion cannot feel any compassion for the zebra or it would have a hard time killing and surviving (although we know they feel compassion and love for their young and pride). As humans, our existence does not depend on killing anymore, so we have the luxury of developing a highly sensitive capacity for compassion toward any and everything.

 


Via Ashish Umre
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PLoS Computational Biology: The Impact of Imitation on Vaccination Behavior in Social Contact Networks

PLoS Computational Biology: The Impact of Imitation on Vaccination Behavior in Social Contact Networks | Social Simulation | Scoop.it

Previous game-theoretic studies of vaccination behavior typically have often assumed that populations are homogeneously mixed and that individuals are fully rational. In reality, there is heterogeneity in the number of contacts per individual, and individuals tend to imitate others who appear to have adopted successful strategies. Here, we use network-based mathematical models to study the effects of both imitation behavior and contact heterogeneity on vaccination coverage and disease dynamics. We integrate contact network epidemiological models with a framework for decision-making, within which individuals make their decisions either based purely on payoff maximization or by imitating the vaccination behavior of a social contact. Simulations suggest that when the cost of vaccination is high imitation behavior may decrease vaccination coverage. However, when the cost of vaccination is small relative to that of infection, imitation behavior increases vaccination coverage, but, surprisingly, also increases the magnitude of epidemics through the clustering of non-vaccinators within the network. Thus, imitation behavior may impede the eradication of infectious diseases. Calculations that ignore behavioral clustering caused by imitation may significantly underestimate the levels of vaccination coverage required to attain herd immunity.

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Network Science

NEW JOURNAL IN 2013Network Science is a new journal for a new discipline -- one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social,...
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Graph spectra and the detectability of community structure in networks

We study networks that display community structure -- groups of nodes within which connections are unusually dense. Using methods from random matrix theory, we calculate the spectra of such networks in the limit of large size, and hence demonstrate the presence of a phase transition in matrix methods for community detection, such as the popular modularity maximization method. The transition separates a regime in which such methods successfully detect the community structure from one in which the structure is present but is not detected. By comparing these results with recent analyses of maximum-likelihood methods we are able to show that spectral modularity maximization is an optimal detection method in the sense that no other method will succeed in the regime where the modularity method fails.

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PhD position on the analysis and modelling of animal social networks

PhD position on the analysis and modelling of animal social networks | Social Simulation | Scoop.it

Our group based at ETH Zurich, Switzerland, is looking for a PhD candidate to work on the analysis of dynamical social networks and the adaptive benefit of individual strategies in a population of wild house mice.
Name: Nicolas Perony
Email: nperony@ethz.ch


Via Complexity Digest
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Network Science

Network Science is a new journal for a new discipline -- one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it.


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I Wanted to Predict Elections with Twitter and all I got was this Lousy Paper

Predicting X from Twitter is a popular fad within the Twitter research subculture. It seems both appealing and relatively easy. Among such kind of studies, electoral prediction is maybe the most attractive, and at this moment there is a growing body of literature on such a topic. This is not only an interesting research problem but, above all, it is extremely difficult. However, most of the authors seem to be more interested in claiming positive results than in providing sound and reproducible methods. It is also especially worrisome that many recent papers seem to only acknowledge those studies supporting the idea of Twitter predicting elections, instead of conducting a balanced literature review showing both sides of the matter. After reading many of such papers I have decided to write such a survey myself. Hence, in this paper, every study relevant to the matter of electoral prediction using social media is commented. From this review it can be concluded that the predictive power of Twitter regarding elections has been greatly exaggerated, and that hard research problems still lie ahead.

 

"I Wanted to Predict Elections with Twitter and all I got was this Lousy Paper" -- A Balanced Survey on Election Prediction using Twitter Data

Daniel Gayo-Avello

http://arxiv.org/abs/1204.6441


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EPJ Data Science - a SpringerOpen journal

EPJ Data Science  - a SpringerOpen journal | Social Simulation | Scoop.it

The 21st century is currently witnessing the establishment of data-driven science as a complementary approach to the traditional hypothesis-driven method. This (r)evolution accompanying the paradigm shift from reductionism to complex systems sciences has already largely transformed the natural sciences and is about to bring the same changes to the techno-socio-economic sciences, viewed broadly.

 

Editors-in-Chief
Frank Schweitzer, ETH Zürich
Alessandro Vespignani, Northeastern University


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