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SOCNET  June 2015

SOCNET June 2015

Subject:

Selected Latest Complexity Digest Posts (fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 8 Jun 2015 10:03:58 -0400

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

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TEXT/PLAIN (231 lines)

***** To join INSNA, visit http://www.insna.org *****


   Barry Wellman
  _______________________________________________________________________
   FRSC INSNA Founder University of Toronto
   http://www.chass.utoronto.ca/~wellman twitter: @barrywellman
   NETWORKED:The New Social Operating System. Lee Rainie & Barry Wellman
   MIT Press http://amzn.to/zXZg39 Print $14 Kindle $9
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 8 Jun 2015 11:02:30 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
Reply-To: [log in to unmask]
To: "[utf-8] Barry" <[log in to unmask]>
Subject: [utf-8] Latest Complexity Digest Posts

Learn about the latest and greatest related to complex systems research. More at http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=b418bfcddd&e=55e25a0e3e



Exposure to ideologically diverse news and opinion on Facebook

    Exposure to news, opinion, and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using deidentified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks and examined the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook˙˙s algorithmically ranked News Feed and further studied users˙˙ choices to click through to ideologically discordant content. Compared with algorithmic ranking, individuals˙˙ choices played a stronger role in limiting exposure to cross-cutting content.

Exposure to ideologically diverse news and opinion on Facebook
Eytan Bakshy, Solomon Messing, Lada A. Adamic

Science 5 June 2015:
Vol. 348 no. 6239 pp. 1130-1132
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Long-term evolution of techno-social networks: Statistical regularities, predictability and stability of social behaviors

    In social networks, individuals constantly drop ties and replace them by new ones in a highly unpredictable fashion. This highly dynamical nature of social ties has important implications for processes such as the spread of information or of epidemics. Several studies have demonstrated the influence of a number of factors on the intricate microscopic process of tie replacement, but the macroscopic long-term effects of such changes remain largely unexplored. Here we investigate whether, despite the inherent randomness at the microscopic level, there are macroscopic statistical regularities in the long-term evolution of social networks. In particular, we analyze the email network of a large organization with over 1,000 individuals throughout four consecutive years. We find that, although the evolution of individual ties is highly unpredictable, the macro-evolution of social communication networks follows well-defined statistical laws, characterized by exponentially decaying
log-variations of the weight of social ties and of individuals' social strength. At the same time, we find that individuals have social signatures and communication strategies that are remarkably stable over the scale of several years.

Long-term evolution of techno-social networks: Statistical regularities, predictability and stability of social behaviors
Antonia Godoy-Lorite, Roger Guimera, Marta Sales-Pardo

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Emergence, self-organization and network efficiency in gigantic termite-nest-networks build using simple rules

    Termites, like many social insects, build nests of complex architecture. These constructions have been proposed to optimize different structural features. Here we describe the nest network of the termite Nasutitermes ephratae, which is among the largest nest-network reported for termites and show that it optimizes diverse parameters defining the network architecture. The network structure avoids multiple crossing of galleries and minimizes the overlap of foraging territories. Thus, these termites are able to minimize the number of galleries they build, while maximizing the foraging area available at the nest mounds. We present a simple computer algorithm that reproduces the basics characteristics of this termite nest network, showing that simple rules can produce complex architectural designs efficiently.

Emergence, self-organization and network efficiency in gigantic termite-nest-networks build using simple rules
Diego Griffon, Carmen Andara, Klaus Jaffe

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Kinetics of Social Contagion

    Diffusion of information, behavioural patterns or innovations follows diverse pathways depending on a number of conditions, including the structure of the underlying social network, the sensitivity to peer pressure and the influence of media. Here we study analytically and by simulations a general model that incorporates threshold mechanism capturing sensitivity to peer pressure, the effect of `immune' nodes who never adopt, and a perpetual flow of external information. While any constant, non-zero rate of dynamically-introduced innovators leads to global spreading, the kinetics by which the asymptotic state is approached show rich behaviour. In particular we find that, as a function of the density of immune nodes, there is a transition from fast to slow spreading governed by entirely different mechanisms. This transition happens below the percolation threshold of fragmentation of the network, and has its origin in the competition between cascading behaviour induced by
innovators and blocking of adoption due to immune nodes. This change is accompanied by a percolation transition of the induced clusters.

Kinetics of Social Contagion
Zhongyuan Ruan, Gerardo Iniguez, Marton Karsai, Janos Kertesz

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Generalized communities in networks

    A substantial volume of research has been devoted to studies of
community structure in networks, but communities are not the only possible
form of large-scale network structure. Here we describe a broad extension
of community structure that encompasses traditional communities but
includes a wide range of generalized structural patterns as well. We
describe a principled method for detecting this generalized structure in
empirical network data and demonstrate with real-world examples how it can
be used to learn new things about the shape and meaning of networks.

Generalized communities in networks
M. E. J. Newman, Tiago P. Peixoto

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The anatomy of urban social networks and its implications in the searchability problem

    The appearance of large geolocated communication datasets has recently
increased our understanding of how social networks relate to their
physical space. However, many recurrently reported properties, such as the
spatial clustering of network communities, have not yet been
systematically tested at different scales. In this work we analyze the
social network structure of over 25 million phone users from three
countries at three different scales: country, provinces and cities. We
consistently find that this last urban scenario presents significant
differences to common knowledge about social networks. First, the
emergence of a giant component in the network seems to be controlled by
whether or not the network spans over the entire urban border, almost
independently of the population or geographic extension of the city.
Second, urban communities are much less geographically clustered than
expected. These two findings shed new light on the widely-studied
searchability in self-organized networks. By exhaustive simulation of
decentralized search strategies we conclude that urban networks are
searchable not through geographical proximity as their country-wide
counterparts, but through an homophily-driven community structure.

The anatomy of urban social networks and its implications in the searchability problem
C. Herrera-Yagüe, C.M. Schneider, T. Couronné, Z. Smoreda, R.M. Benito, P.J. Zufiria, M.C. González

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Isomorphisms in Multilayer Networks

    We extend the concept of graph isomorphisms to multilayer networks, and we identify multiple types of isomorphisms. For example, in multilayer networks with a single "aspect" (i.e., type of layering), permuting vertex labels, layer labels, and both of types of layers each yield a different type of isomorphism. We discuss how multilayer network isomorphisms naturally lead to defining isomorphisms in any type of network that can be represented as a multilayer network. This thereby yields isomorphisms for multiplex networks, temporal networks, networks with both such features, and more. We reduce each of the multilayer network isomorphism problems to a graph isomorphism problem, and we use this reduction to prove that the multilayer network isomorphism problem is computationally equally hard as the graph isomorphism problem. One can thus use software that has been developed to solve graph isomorphism problems as a practical means for solving multilayer network isomorphism
problems.

Isomorphisms in Multilayer Networks
Mikko Kivelä, Mason A. Porter

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Simulating Social Complexity (by Bruce Edmonds)

    Social systems are among the most complex known. This poses particular problems for those who wish to understand them. The complexity often makes analytic approaches infeasible and natural language approaches inadequate for relating intricate cause and effect. However, individual- and agent-based computational approaches hold out the possibility of new and deeper understanding of such systems.

Simulating Social Complexity examines all aspects of using agent- or individual-based simulation. This approach represents systems as individual elements having each their own set of differing states and internal processes. The interactions between elements in the simulation represent interactions in the target systems. What makes these elements "social" is that they are usefully interpretable as interacting elements of an observed society. In this, the focus is on human society, but can be extended to include social animals or artificial agents where such work enhances our understanding of human society.

The phenomena of interest then result (emerge) from the dynamics of the interaction of social actors in an essential way and are usually not easily simplifiable by, for example, considering only representative actors.

The introduction of accessible agent-based modelling allows the representation of social complexity in a more natural and direct manner than previous techniques. In particular, it is no longer necessary to distort a model with the introduction of overly strong assumptions simply in order to obtain analytic tractability. This makes agent-based modelling relatively accessible to a range of scientists. The outcomes of such models can be displayed and animated in ways that also make them more interpretable by experts and stakeholders.



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Five postdoctoral fellowships in complex systems, UNAM

    As a part of the consolidation of the National Laboratory of Complexity, the Center for Complexity Science of the National Autonomous University of Mexico is seeking outstanding candidates for five one year postdoctoral positions beginning in August, 2015. Research plans from all areas related to complex systems are encouraged.

Please send CV and research plan to cgg [at] unam.mx before June 10th.

//Please forward to whom may be interested.˙˙˙˙

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How random are complex networks

    Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks---the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain---and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations, and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation
of network randomness.

How random are complex networks
Chiara Orsini, Marija Mitrovi˙˙ Dankulov, Almerima Jamakovic, Priya Mahadevan, Pol Colomer-de-Simón, Amin Vahdat, Kevin E. Bassler, Zoltán Toroczkai, Marián Boguñá, Guido Caldarelli, Santo Fortunato, Dmitri Krioukov

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A First Course in Network Theory (by Ernesto Estrada & Philip Knight)

    The study of network theory is a highly interdisciplinary field, which has emerged as a major topic of interest in various disciplines ranging from physics and mathematics, to biology and sociology. This book promotes the diverse nature of the study of complex networks by balancing the needs of students from very different backgrounds. It references the most commonly used concepts in network theory, provides examples of their applications in solving practical problems, and clear indications on how to analyse their results.

In the first part of the book, students and researchers will discover the quantitative and analytical tools necessary to work with complex networks, including the most basic concepts in network and graph theory, linear and matrix algebra, as well as the physical concepts most frequently used for studying networks. They will also find instruction on some key skills such as how to proof analytic results and how to manipulate empirical network data. The bulk of the text is focused on instructing readers on the most useful tools for modern practitioners of network theory. These include degree distributions, random networks, network fragments, centrality measures, clusters and communities, communicability, and local and global properties of networks. The combination of theory, example and method that are presented in this text, should ready the student to conduct their own analysis of networks with confidence and allow teachers to select appropriate examples and problems to teach
this subject in the classroom.



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Who Gets What - and Why: The New Economics of Matchmaking and Market Design (by Alvin E. Roth)

    A Nobel laureate reveals the often surprising rules that govern a vast array of activities ˙˙ both mundane and life-changing ˙˙ in which money may play little or no role.

If you˙˙ve ever sought a job or hired someone, applied to college or
guided your child into a good kindergarten, asked someone out on a date or
been asked out, you˙˙ve participated in a kind of market. Most of the
study of economics deals with commodity markets, where the price of a good
connects sellers and buyers. But what about other kinds of ˙˙goods,˙˙ like
a spot in the Yale freshman class or a position at Google? This is the
territory of matching markets, where ˙˙sellers˙˙ and ˙˙buyers˙˙ must
choose each other, and price isn˙˙t the only factor determining who gets
what.

Alvin E. Roth is one of the world˙˙s leading experts on matching markets. He has even designed several of them, including the exchange that places medical students in residencies and the system that increases the number of kidney transplants by better matching donors to patients. In Who Gets What ˙˙ And Why, Roth reveals the matching markets hidden around us and shows how to recognize a good match and make smarter, more confident decisions.



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==============================================
Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

You can contribute to Complexity Digest selecting one of our topics (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=d7598affe9&e=55e25a0e3e ) and using the "Suggest" button.
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