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SOCNET  July 2015

SOCNET July 2015

Subject:

Re: Selected Latest Complexity Digest Posts

From:

"Glasgow, Kimberly A." <[log in to unmask]>

Reply-To:

Glasgow, Kimberly A.

Date:

Mon, 6 Jul 2015 21:52:16 +0000

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text/plain

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text/plain (459 lines)

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

Ok, Iıll check those out.

On 6/8/15, 10:03 AM, "Barry Wellman" <[log in to unmask]> wrote:

>*****  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
>http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id
>=5be14a443f&e=55e25a0e3e
>
>See it on Scoop.it
>(http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=fc758c351a&e=55e25a0e3e) , via Papers
>(http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&
>id=4dc7b035b4&e=55e25a0e3e)
>
>
>
>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
>
>http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=911aef5b6a&e=55e25a0e3e
>
>See it on Scoop.it
>(http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&
>id=90c5c21a29&e=55e25a0e3e) , via Papers
>(http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&
>id=a96f20034c&e=55e25a0e3e)
>
>
>
>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
>
>http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=34e3ebeb48&e=55e25a0e3e
>
>See it on Scoop.it
>(http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&
>id=b225deecac&e=55e25a0e3e) , via Papers
>(http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=3bdfe89576&e=55e25a0e3e)
>
>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
>
>http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id
>=2c05d1076b&e=55e25a0e3e
>
>See it on Scoop.it
>(http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=b0d74c6ac5&e=55e25a0e3e) , via Papers
>(http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=599e1a4599&e=55e25a0e3e)
>
>
>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
>
>http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id
>=a56e377e3e&e=55e25a0e3e
>
>See it on Scoop.it
>(http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&
>id=a792ff90b1&e=55e25a0e3e) , via Papers
>(http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=45eb686dbd&e=55e25a0e3e)
>
>
>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
>
>http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id
>=15fbdbe5ae&e=55e25a0e3e
>
>See it on Scoop.it
>(http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&
>id=0922d657de&e=55e25a0e3e) , via Papers
>(http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=e5b7f2c203&e=55e25a0e3e)
>
>
>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
>
>http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=fafdc7e51a&e=55e25a0e3e
>
>See it on Scoop.it
>(http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=9cefe28c14&e=55e25a0e3e) , via Papers
>(http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=b3599e1782&e=55e25a0e3e)
>
>
>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.
>
>
>
>See it on Scoop.it
>(http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&
>id=287316ae66&e=55e25a0e3e) , via CxBooks
>(http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=dea71c6eab&e=55e25a0e3e)
>
>
>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.˙˙˙˙
>
>http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=a8c0723477&e=55e25a0e3e
>
>See it on Scoop.it
>(http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&
>id=8ab5396732&e=55e25a0e3e) , via CxAnnouncements
>(http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&
>id=790425be74&e=55e25a0e3e)
>
>
>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
>
>http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=f4ce23aac2&e=55e25a0e3e
>
>See it on Scoop.it
>(http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&
>id=520c064319&e=55e25a0e3e) , via Papers
>(http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=acfd2b1cf0&e=55e25a0e3e)
>
>
>
>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.
>
>
>
>See it on Scoop.it
>(http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=f6a1798c2c&e=55e25a0e3e) , via CxBooks
>(http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=ba0b46aa69&e=55e25a0e3e)
>
>
>
>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.
>
>
>
>See it on Scoop.it
>(http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&
>id=a2067182bd&e=55e25a0e3e) , via CxBooks
>(http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&i
>d=d3af6e93d5&e=55e25a0e3e)
>
>==============================================
>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&i
>d=d7598affe9&e=55e25a0e3e ) and using the "Suggest" button.
>==============================================
>==============================================
>
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>_____________________________________________________________________
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