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SOCNET  August 2015

SOCNET August 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, 10 Aug 2015 08:25:58 -0400

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (173 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, 10 Aug 2015 11:03:51 +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-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=741e69dfa4&e=55e25a0e3e



The New Laws of Explosive Networks

    Researchers are uncovering the hidden laws that reveal how the Internet grows, how viruses spread, and how financial bubbles burst.

http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=48be07a367&e=55e25a0e3e

See it on Scoop.it (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=141670d31f&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=a06e4e8954&e=55e25a0e3e)

BARRY: Jeff Boase and I wrote about viral similarities years ago. See my 
online vitae for details


Network science: Destruction perfected

    Pinpointing the nodes whose removal most effectively disrupts a network has become a lot easier with the development of an efficient algorithm. Potential applications might include cybersecurity and disease control.

Network science: Destruction perfected
˙˙ István A. Kovács & Albert-László Barabási

Nature 524, 38˙˙39 (06 August 2015) http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=2ab33ef59e&e=55e25a0e3e ;

See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=28b8002da9&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=276df87169&e=55e25a0e3e)



A survey of results on mobile phone datasets analysis

    In this paper, we review some advances made recently in the study of mobile phone datasets. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized datasets, and has grown into a stand-alone topic. We survey the contributions made so far on the social networks that can be constructed with such data, the study of personal mobility, geographical partitioning, urban planning, and help towards development as well as security and privacy issues.

A survey of results on mobile phone datasets analysis
Vincent D Blondel, Adeline Decuyper and Gautier Krings

EPJ Data Science 2015, 4:10

http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=489f53518a&e=55e25a0e3e ;

See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=a0cd9c7aba&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=c70a29d2ec&e=55e25a0e3e)


Entropy, Information and Complexity or Which Aims the Arrow of Time?

    In this article, we analyze the interrelationships among such notions as entropy, information, complexity, order and chaos and show using the theory of categories how to generalize the second law of thermodynamics as a law of increasing generalized entropy or a general law of complification. This law could be applied to any system with morphisms, including all of our universe and its subsystems. We discuss how such a general law and other laws of nature drive the evolution of the universe, including physicochemical and biological evolutions. In addition, we determine eliminating selection in physicochemical evolution as an extremely simplified prototype of natural selection. Laws of nature do not allow complexity and entropy to reach maximal values by generating structures. One could consider them as a kind of ˙˙breeder˙˙ of such selection.

Entropy, Information and Complexity or Which Aims the Arrow of Time?
George E. Mikhailovsky  and Alexander P. Levich

Entropy 2015, 17(7), 4863-4890; http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=a782f025be&e=55e25a0e3e ;

See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=7a7782d2b9&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=ba6df05f73&e=55e25a0e3e)



Exploring Temporal Networks with Greedy Walks

    Temporal networks come with a wide variety of heterogeneities, from burstiness of event sequences to correlations between timingsof node and link activations. In this paper, we set to explore the latter by using greedy walks as probes of temporal network structure. Given a temporal network (a sequence of contacts), greedy walks proceed from node to node by always following the first available contact. Because of this, their structure is particularly sensitive to temporal-topological patterns involving repeated contacts between sets of nodes. This becomes evident in their small coverage per step as compared to a temporal reference model -- in empirical temporal networks, greedy walks often get stuck within small sets of nodes because of correlated contact patterns. While this may also happen in static networks that have pronounced community structure, the use of the temporal reference model takes the underlying static network structure out of the equation and indicates that
there is a purely temporal reason for the observations. Further analysis of the structure of greedy walks indicates that burst trains, sequences of repeated contacts between node pairs, are the dominant factor. However, there are larger patterns too, as shown with non-backtracking greedy walks. We proceed further to study the entropy rates of greedy walks, and show that the sequences of visited nodes are more structured and predictable in original data as compared to temporally uncorrelated references. Taken together, these results indicate a richness of correlated temporal-topological patterns in temporal networks.

Exploring Temporal Networks with Greedy Walks
Jari Saramaki, Petter Holme

http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=b5ebbfa892&e=55e25a0e3e

See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=07efc35cea&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=ea2b16197c&e=55e25a0e3e)



Paradoxical Interpretations of Urban Scaling Laws

    Scaling laws are powerful summaries of the variations of urban attributes with city size. However, the validity of their universal meaning for cities is hampered by the observation that different scaling regimes can be encountered for the same territory, time and attribute, depending on the criteria used to delineate cities. The aim of this paper is to present new insights concerning this variation, coupled with a sensitivity analysis of urban scaling in France, for several socio-economic and infrastructural attributes from data collected exhaustively at the local level. The sensitivity analysis considers different aggregations of local units for which data are given by the Population Census. We produce a large variety of definitions of cities (approximatively 5000) by aggregating local Census units corresponding to the systematic combination of three definitional criteria: density, commuting flows and population cutoffs. We then measure the magnitude of scaling estimations
and their sensitivity to city definitions for several urban indicators, showing for example that simple population cutoffs impact dramatically on the results obtained for a given system and attribute. Variations are interpreted with respect to the meaning of the attributes (socio-economic descriptors as well as infrastructure) and the urban definitions used (understood as the combination of the three criteria). Because of the Modifiable Areal Unit Problem and of the heterogeneous morphologies and social landscapes in the cities internal space, scaling estimations are subject to large variations, distorting many of the conclusions on which generative models are based. We conclude that examining scaling variations might be an opportunity to understand better the inner composition of cities with regard to their size, i.e. to link the scales of the city-system with the system of cities.

Paradoxical Interpretations of Urban Scaling Laws
Clementine Cottineau, Erez Hatna, Elsa Arcaute, Michael Batty

http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=eb081aef62&e=55e25a0e3e

See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=ca4a45f825&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=ab4b7138f3&e=55e25a0e3e)


Temporal Pattern of Communication Spike Trains in Twitter: How Often, Who Interacts with Whom?

    We evaluate complex time series of online user communication in Twitter social network. We construct spike trains of each user participating any interaction with any other users in the network. Retweet a message, mention a user in a message, and reply to a message are types of interaction observed in Twitter. By applying the local variation originally established for neuron spike trains, we quantify the temporal behavior of active and passive but popular users separately. We show that the local variation of active users give bursts independent of the activation frequency. On the other hand, the local variation of popular users present irregular random (Poisson) patterns and the resultant temporal patterns are highly influenced by the frequency of the attention, e.g. bursts for less popular users, but randomly distributed temporarily uncorrelated spikes for most popular users. To understand the coincidence in the temporal patterns of two distinct interactions, we propose
linear correlations of the local variation of the filtered spikes based on concerned interactions. We conclude that the local variations of the retweet and mention spike trains provide a good agreement only for most popular users, which suggests that the dynamics of mention a user together with that of retweet is a better identity of popular users instead of only paying attention of the dynamics of retweet, a conventional measure of user popularity.

Temporal Pattern of Communication Spike Trains in Twitter: How Often, Who Interacts with Whom?
Ceyda Sanl˙˙, Renaud Lambiotte

http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=da2d2c2be8&e=55e25a0e3e

See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=7e23652388&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=3aa23583fd&e=55e25a0e3e)


César Hidalgo on Why Information Grows

    César visits the RSA to present a new view of the relationship between individual and collective knowledge, linking information theory, economics and biology to explain the deep evolution of social and economic systems.
In a radical rethink of what an economy is, one of WIRED magazine˙˙s 50 People Who Could Change the World, César Hidalgo argues that it is the measure of a nation˙˙s cultural complexity ˙˙ the nexus of people, ideas and invention - rather than its GDP or per-capita income, that explains the success or failure of its economic performance. To understand the growth of economies, Hidalgo argues, we first need to understand the growth of order itself.

http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=43a42da279&e=55e25a0e3e

See it on Scoop.it (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=1b0e134d3c&e=55e25a0e3e) , via Talks (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=ab00bc55bb&e=55e25a0e3e)



Quantifying Controversy in Social Media

    Which topics spark the most heated debates in social media? Identifying these topics is a first step towards creating systems which pierce echo chambers. In this paper, we perform the first systematic methodological study of controversy detection using social-media network structure and content.
Unlike previous work, rather than identifying controversy in a single hand-picked topic and use domain-specific knowledge, we focus on comparing topics in any domain. Our approach to quantifying controversy is a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic, which represents alignment of opinion among users; (ii) partitioning the conversation graph to identify potential sides of controversy; and (iii) measuring the amount of controversy from characteristics of the graph.
We perform an extensive comparison of controversy measures, as well as graph building approaches and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task.

Quantifying Controversy in Social Media
Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, Michael Mathioudakis

http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=f937c95c43&e=55e25a0e3e

See it on Scoop.it (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=71e558adde&e=55e25a0e3e) , via Papers (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=d0993ad1f8&e=55e25a0e3e)


The success of complex networks at criticality

    In spiking neural networks an action potential could in principle trigger subsequent spikes in the neighbourhood of the initial neuron. A successful spike is that which trigger subsequent spikes giving rise to cascading behaviour within the system. In this study we introduce a metric to assess the success of spikes emitted by integrate-and-fire neurons arranged in complex topologies and whose collective behaviour is undergoing a phase transition that is identified by neuronal avalanches that become clusters of activation whose distribution of sizes can be approximated by a power-law. In numerical simulations we report that scale-free networks with the small-world property is the structure in which neurons possess more successful spikes. As well, we conclude both analytically and in numerical simulations that fully-connected networks are structures in which neurons perform worse. Additionally, we study how the small-world property affects spiking behaviour and its success in
scale-free networks.

The success of complex networks at criticality
Victor Hernandez-Urbina, Tom L. Underwood, J. Michael Herrmann

http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=f5ebbdd1fd&e=55e25a0e3e

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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=a909d7f923&e=55e25a0e3e ) and using the "Suggest" button.
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