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   Barry Wellman
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Date: Mon, 7 Apr 2014 11:10:26 -0500
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Subject: [comdig] Latest Complexity Digest Posts

Learn about the latest and greatest related to complex systems research. More at http://comdig.unam.mx



Contributions and challenges for network models in cognitive neuroscience

    The confluence of new approaches in recording patterns of brain connectivity and quantitative analytic tools from network science has opened new avenues toward understanding the organization and function of brain networks. Descriptive network models of brain structural and functional connectivity have made several important contributions; for example, in the mapping of putative network hubs and network communities. Building on the importance of anatomical and functional interactions, network models have provided insight into the basic structures and mechanisms that enable integrative neural processes. Network models have also been instrumental in understanding the role of structural brain networks in generating spatially and temporally organized brain activity. Despite these contributions, network models are subject to limitations in methodology and interpretation, and they face many challenges as brain connectivity data sets continue to increase in detail and complexity.

Contributions and challenges for network models in cognitive neuroscience
˙˙ Olaf Sporns
Nature Neuroscience (2014) http://dx.doi.org/10.1038/nn.3690

See it on Scoop.it (http://www.scoop.it/t/papers/p/4018912730/2014/04/06/contributions-and-challenges-for-network-models-in-cognitive-neuroscience) , via Papers (http://www.scoop.it/t/papers)



Dynamical Systems on Networks: A Tutorial

    We give a tutorial for the study of dynamical systems on networks, and we focus in particular on ``simple" situations that are tractable analytically. We briefly motivate why examining dynamical systems on networks is interesting and important. We then give several fascinating examples and discuss some theoretical results. We also discuss dynamical systems on dynamical (i.e., time-dependent) networks, overview software implementations, and give our outlook on the field.

Dynamical Systems on Networks: A Tutorial
Mason A. Porter, James P. Gleeson

http://arxiv.org/abs/1403.7663

See it on Scoop.it (http://www.scoop.it/t/papers/p/4018934177/2014/04/05/dynamical-systems-on-networks-a-tutorial) , via Papers (http://www.scoop.it/t/papers)



Innovations in Statistical Physics

    In 1963-71, a group of people, myself included, formulated and perfected a new approach to physics problems, which eventually came to be known under the names of scaling, universality, and renormalization. This work formed the basis of a wide variety of theories ranging from its starting point in critical phenomena, and moving out to particle physics and relativity and then into economics and biology. This work was of transcendental beauty and of considerable intellectual importance.
This left me with a personal problem. What next? Constructing the answer to that question would dominate the next 45 years of my professional life. I would try to:
* Help in finding and constructing new fields of science
* Do research and give talks on science/society borderline
* Provide helpful, constructive criticism of scientific and technical work
* Help students and younger scientists
* Demonstrate scientific leadership

Innovations in Statistical Physics
Leo P. Kadanoff

http://arxiv.org/abs/1403.6464

See it on Scoop.it (http://www.scoop.it/t/papers/p/4018995917/2014/04/05/innovations-in-statistical-physics) , via Papers (http://www.scoop.it/t/papers)



Dynamics of infectious diseases

    Modern infectious disease epidemiology has a strong history of using mathematics both for prediction and to gain a deeper understanding. However the study of infectious diseases is a highly interdisciplinary subject requiring insights from multiple disciplines, in particular a biological knowledge of the pathogen, a statistical description of the available data and a mathematical framework for prediction. Here we begin with the basic building blocks of infectious disease epidemiology˙˙the SIS and SIR type models˙˙before considering the progress that has been made over the recent decades and the challenges that lie ahead. Throughout we focus on the understanding that can be developed from relatively simple models, although accurate prediction will inevitably require far greater complexity beyond the scope of this review. In particular, we focus on three critical aspects of infectious disease models that we feel fundamentally shape their dynamics: heterogeneously structured
populations, stochasticity and spatial structure. Throughout we relate the mathematical models and their results to a variety of real-world problems.

Kat Rock et al 2014 Rep. Prog. Phys. 77 026602
http://dx.doi.org/10.1088/0034-4885/77/2/026602 ;

See it on Scoop.it (http://www.scoop.it/t/papers/p/4018999032/2014/04/05/dynamics-of-infectious-diseases) , via Papers (http://www.scoop.it/t/papers)



Cross-checking different sources of mobility information

    The pervasive use of new mobile devices has allowed a better characterization in space and time of human concentrations and mobility in general. Besides its theoretical interest, describing mobility is of great importance for a number of practical applications ranging from the forecast of disease spreading to the design of new spaces in urban environments. While classical data sources, such as surveys or census, have a limited level of geographical resolution (e.g., districts, municipalities, counties are typically used) or are restricted to generic workdays or weekends, the data coming from mobile devices can be precisely located both in time and space. Most previous works have used a single data source to study human mobility patterns. Here we perform instead a cross-check analysis by comparing results obtained with data collected from three different sources: Twitter, census and cell phones. The analysis is focused on the urban areas of Barcelona and Madrid, for which data
of the three types is available. We assess the correlation between the datasets on different aspects: the spatial distribution of people concentration, the temporal evolution of people density and the mobility patterns of individuals. Our results show that the three data sources are providing comparable information. Even though the representativeness of Twitter geolocated data is lower than that of mobile phone and census data, the correlations between the population density profiles and mobility patterns detected by the three datasets are close to one in a grid with cells of 2x2 and 1x1 square kilometers. This level of correlation supports the feasibility of interchanging the three data sources at the spatio-temporal scales considered.

Cross-checking different sources of mobility information
Maxime Lenormand, Miguel Picornell, Oliva G. Cantu-Ros, Antonia Tugores, Thomas Louail, Ricardo Herranz, Marc Barthelemy, Enrique Frias-Martinez, Jose J. Ramasco

http://arxiv.org/abs/1404.0333

See it on Scoop.it (http://www.scoop.it/t/papers/p/4018994814/2014/04/04/cross-checking-different-sources-of-mobility-information) , via Papers (http://www.scoop.it/t/papers)





Followers Are Not Enough: Beyond Structural Communities in Online Social Networks

    Community detection in online social networks is typically based on the analysis of the explicit connections between users, such as "friends" on Facebook and "followers" on Twitter. But online users often have hundreds or even thousands of such connections, and many of these connections do not correspond to real friendships or more generally to accounts that users interact with. We claim that community detection in online social networks should be question-oriented and rely on additional information beyond the simple structure of the network. The concept of 'community' is very general, and different questions such as "who do we interact with?" and "with whom do we share similar interests?" can lead to the discovery of different social groups. In this paper we focus on three types of communities beyond structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to
highlight these three community types, and then infer the communities associated with these weightings. We show that the communities obtained in the three weighted cases are highly different from each other, and from the communities obtained by considering only the unweighted structural network. Our results confirm that asking a precise question is an unavoidable first step in community detection in online social networks, and that different questions can lead to different insights into the network under study.

Followers Are Not Enough: Beyond Structural Communities in Online Social Networks
David Darmon, Elisa Omodei, Joshua Garland

http://arxiv.org/abs/1404.0300

See it on Scoop.it (http://www.scoop.it/t/papers/p/4018996515/2014/04/04/followers-are-not-enough-beyond-structural-communities-in-online-social-networks) , via Papers (http://www.scoop.it/t/papers)




The Informative Herd: why humans and other animals imitate more when conditions are adverse

    Decisions in a group often result in imitation and aggregation, which are enhanced in panic, dangerous, stressful or negative situations. Current explanations of this enhancement are restricted to particular contexts, such as anti-predatory behavior, deflection of responsibility in humans, or cases in which the negative situation is associated with an increase in uncertainty. But this effect is observed across taxa and in very diverse conditions, suggesting that it may arise from a more general cause, such as a fundamental characteristic of social decision-making. Current decision-making theories do not explain it, but we noted that they concentrate on estimating which of the available options is the best one, implicitly neglecting the cases in which several options can be good at the same time. We explore a more general model of decision-making that instead estimates the probability that each option is good, allowing several options to be good simultaneously. This model
predicts with great generality the enhanced imitation in negative situations. Fish and human behavioral data showing an increased imitation behavior in negative circumstances are well described by this type of decisions to choose a good option.

The Informative Herd: why humans and other animals imitate more when conditions are adverse
Alfonso Pérez-Escudero, Gonzalo G. de Polavieja

http://arxiv.org/abs/1403.7478

See it on Scoop.it (http://www.scoop.it/t/papers/p/4018933143/2014/04/03/the-informative-herd-why-humans-and-other-animals-imitate-more-when-conditions-are-adverse) , via Papers (http://www.scoop.it/t/papers)



A solution to the collective action problem in between-group conflict with within-group inequality

    Conflict with conspecifics from neighbouring groups over territory, mating opportunities and other resources is observed in many social organisms, including humans. Here we investigate the evolutionary origins of social instincts, as shaped by selection resulting from between-group conflict in the presence of a collective action problem. We focus on the effects of the differences between individuals on the evolutionary dynamics. Our theoretical models predict that high-rank individuals, who are able to usurp a disproportional share of resources in within-group interactions, will act seemingly altruistically in between-group conflict, expending more effort and often having lower reproductive success than their low-rank group-mates. Similar behaviour is expected for individuals with higher motivation, higher strengths or lower costs, or for individuals in a leadership position. Our theory also provides an evolutionary foundation for classical equity theory, and it has
implications for the origin of coercive leadership and for reproductive skew theory.

A solution to the collective action problem in between-group conflict with within-group inequality
˙˙ Sergey Gavrilets & Laura Fortunato

Nature Communications 5, Article number: 3526 http://dx.doi.org/10.1038/ncomms4526

See it on Scoop.it (http://www.scoop.it/t/papers/p/4018875538/2014/04/02/a-solution-to-the-collective-action-problem-in-between-group-conflict-with-within-group-inequality) , via Papers (http://www.scoop.it/t/papers)


Complexity in Animal Communication: Estimating the Size of N-Gram Structures

    In this paper, new techniques that allow conditional entropy to estimate the combinatorics of symbols are applied to animal communication studies to estimate the communication˙˙s repertoire size. By using the conditional entropy estimates at multiple orders, the paper estimates the total repertoire sizes for animal communication across bottlenose dolphins, humpback whales and several species of birds for an N-gram length of one to three. In addition to discussing the impact of this method on studies of animal communication complexity, the reliability of these estimates is compared to other methods through simulation. While entropy does undercount the total repertoire size due to rare N-grams, it gives a more accurate picture of the most frequently used repertoire than just repertoire size alone.

Complexity in Animal Communication: Estimating the Size of N-Gram Structures
Reginald Smith

Entropy 2014, 16(1), 526-542; http://dx.doi.org/10.3390/e16010526

Help fund the open access fee!
https://www.kickstarter.com/projects/683516221/dolphin-and-whale-language-research-paper-funding

See it on Scoop.it (http://www.scoop.it/t/papers/p/4018868803/2014/04/02/complexity-in-animal-communication-estimating-the-size-of-n-gram-structures) , via Papers (http://www.scoop.it/t/papers)



Modeling Complex Systems for Public Policies ˙˙ a book project

    The Institute for Applied Economic Research (Ipea) ˙˙ a Brazilian think-tank linked to the government ˙˙ is making a request for proposals for eight IDB consultants to contribute with chapters to a seminal book on Complex Systems applied to Public Policies. On one hand, the project aims at pushing forward the modeling frontier, its methodologies and applications for the case of Brazil. On the other hand, the project pursues actual improvement on the understanding of public policies˙˙ mechanisms and effects, through complex systems˙˙ tools and concepts.
The book encompasses five broad themes: (1) concepts and methods; (2) computational tools; (3) public policy phenomena as complex systems (specifically: society, economics, ecology and the cities); (4) applied examples in the world and its emergence in Brazil; and (5) possibilities of prognosis, scenarios and policy-effect analysis using complex systems tools.
The consultant is expected to deliver a proposed extended summary, a preliminary version to be discussed in a seminar in Brazil (July-September 2014) and the final version of the chapter.

http://www.ipea.gov.br/portal/index.php/?option=com_content&view=article&id=21745&Itemid=5

See it on Scoop.it (http://www.scoop.it/t/cxannouncements/p/4018870001/2014/04/02/modeling-complex-systems-for-public-policies-a-book-project) , via CxAnnouncements (http://www.scoop.it/t/cxannouncements)




Sketching a network portrait of the humber region

    Industrial systems can be represented as networks of organizations connected by flows of materials, energy, and money. This network context may produce unexpected consequences in response to policy intervention, so improved understanding is vital; however, industrial network data are commonly unavailable publically. Using a case study in the Humber region, UK, we present a novel methodology of ˙˙network coding˙˙ of semistructured interviews with key industrial and political stakeholders, in combination with an ˙˙industrial taxonomy˙˙ of network archetypes developed to construct an approximation of the region's networks when data are incomplete. This article describes our methodology and presents the resulting network.

Sketching a network portrait of the humber region
Alexandra S. Penn, Paul D. Jensen, Amy Woodward, Lauren Basson, Frank Schiller and Angela Druckman

Complexity

http://dx.doi.org/10.1002/cplx.21519

See it on Scoop.it (http://www.scoop.it/t/papers/p/4018869389/2014/04/02/sketching-a-network-portrait-of-the-humber-region) , via Papers (http://www.scoop.it/t/papers)



Predicting Successful Memes using Network and Community Structure

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

-- To be presented at ICWSM 2014

See it on Scoop.it (http://www.scoop.it/t/papers/p/4018413159/2014/04/02/predicting-successful-memes-using-network-and-community-structure) , via Papers (http://www.scoop.it/t/papers)



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 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.

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

Scientific Reports 4, Article number: 4343 http://dx.doi.org/10.1038/srep04343

See it on Scoop.it (http://www.scoop.it/t/papers/p/4018855608/2014/04/02/the-simple-rules-of-social-contagion) , via Papers (http://www.scoop.it/t/papers)


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