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SOCNET  May 2017

SOCNET May 2017

Subject:

selected Latest Complexity Digest Posts (fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 1 May 2017 09:27:09 -0400

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (140 lines)

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

selected
   Barry Wellman

    A vision is just a vision if it's only in your head
    Step by step, link by link, putting it together
                  Streisand/Sondheim
  _______________________________________________________________________
   NetLab Network                 FRSC                      INSNA Founder
   http://www.chass.utoronto.ca/~wellman           twitter: @barrywellman
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman
                        http://amzn.to/zXZg39
   _______________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 1 May 2017 11:04:07 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
Reply-To: [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=85ecc415fe&e=55e25a0e3e



The Role of Network Analysis in Industrial and Applied Mathematics

    Many problems in industry --- and in the social, natural, information, and medical sciences --- involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. Because the study of networks is concerned explicitly with connectivity between different entities, it has become very prominent in industrial settings, and this importance has been accentuated further amidst the modern data deluge. In this article, we discuss the role of network analysis in industrial and applied mathematics, and we give several examples of network science in industry.


The Role of Network Analysis in Industrial and Applied Mathematics
Mason A. Porter, Sam D. Howison

Source: arxiv.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=030bf65043&e=55e25a0e3e)


Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots

    It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using `social bots' deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple
and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.


Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots

Bjarke Mřnsted, Piotr Sapie˙˙y˙˙ski, Emilio Ferrara, Sune Lehmann

Source: arxiv.org (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=04c32fd368&e=55e25a0e3e)



Reversing the irreversible: from limit cycles to emergent time symmetry

    In 1979 Penrose hypothesized that the arrows of time are explained by the hypothesis that the fundamental laws are time irreversible. That is, our reversible laws, such as the standard model and general relativity are effective, and emerge from an underlying fundamental theory which is time irreversible. In Cort\^{e}s and Smolin (2014a, 2014b, 2016) we put forward a research program aiming at realizing just this. The aim is to find a fundamental description of physics above the planck scale, based on irreversible laws, from which will emerge the apparently reversible dynamics we observe on intermediate scales. Here we continue that program and note that a class of discrete dynamical systems are known to exhibit this very property: they have an underlying discrete irreversible evolution, but in the long term exhibit the properties of a time reversible system, in the form of limit cycles. We connect this to our original model proposal in Cort\^{e}s and Smolin (2014a), and show
that the behaviours obtained there can be explained in terms of the same phenomenon: the attraction of the system to a basin of limit cycles, where the dynamics appears to be time reversible. Further than that, we show that our original models exhibit the very same feature: the emergence of quasi-particle excitations obtained in the earlier work in the space-time description is an expression of the system's convergence to limit cycles when seen in the causal set description.


Reversing the irreversible: from limit cycles to emergent time symmetry
Marina Cortęs, Lee Smolin

Source: arxiv.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=9f20f31c1d&e=55e25a0e3e)



The world of long-range interactions: A bird˙˙s eye view

    In recent years, studies of long-range interacting (LRI) systems have taken centre stage in the arena of statistical mechanics and dynamical system studies, due to new theoretical developments involving tools from as diverse a field as kinetic theory, non-equilibrium statistical mechanics, and large deviation theory, but also due to new and exciting experimental realizations of LRI systems. In this invited contribution, we discuss the general features of long-range interactions, emphasizing in particular the main physical phenomenon of non-additivity, which leads to a plethora of distinct effects, both thermodynamic and dynamic, that are not observed with short-range interactions: Ensemble inequivalence, slow relaxation, broken ergodicity. We also discuss several physical systems with long-range interactions: mean-field spin systems, self-gravitating systems, Euler equations in two dimensions, Coulomb systems, one-component electron plasma, dipolar systems, free-electron
lasers, atoms trapped in optical cavities.


The world of long-range interactions: A bird's eye view
Shamik Gupta, Stefano Ruffo

Source: arxiv.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=21ecadb39e&e=55e25a0e3e)


Robustness and efficiency in interconnected networks with changes in network assortativity

    In this study, the effect of assortativity on the robustness and efficiency of interconnected networks was investigated. This involved constructing a network that possessed the desired degree of assortativity. Additionally, an interconnected network was constructed wherein the assortativity between component networks possessed the desired value. With respect to single networks, the results indicated that a decrease in assortativity provided low hop length, high information diffusion efficiency, and distribution of communication load on edges. The study also revealed that excessive assortativity led to poor network performance. In the study, the assortativity between networks was defined and the following results were demonstrated: assortative connections between networks lowered the average hop length and enhanced information diffusion efficiency, whereas disassortative connections between networks distributed the communication loads of internetwork links and enhanced
robustness. Furthermore, it is necessary to carefully adjust assortativity based on the node degree distribution of networks. Finally, the application of the results to the design of robust and efficient information networks was discussed.


Robustness and efficiency in interconnected networks with changes in network assortativity
Masaya Murakami, Shu Ishikura, Daichi Kominami, Tetsuya Shimokawa and Masayuki Murata
Applied Network Science 2017 2:6
DOI: 10.1007/s41109-017-0025-4

Source: appliednetsci.springeropen.com (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=b7c2aaf1a6&e=55e25a0e3e)


PAFit: An R Package for Modeling and Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks

    Many real-world systems are profitably described as complex networks that grow over time. Preferential attachment and node fitness are two ubiquitous growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and inference. While there are standard statistical packages for estimating the structural properties of complex networks, there is no corresponding package when it comes to the estimation of growth mechanisms. This paper introduces the R package PAFit, which implements well-established statistical methods for estimating preferential attachment and node fitness, as well as a number of functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure good performance for large-scale networks. In this paper, we first introduce the main functionalities of PAFit using simulated
examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks.


PAFit: An R Package for Modeling and Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks
Thong Pham, Paul Sheridan, Hidetoshi Shimodaira

Source: arxiv.org (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=fe872d3be1&e=55e25a0e3e)



Complexity Theory and Dynamical Systems | Demetri Kofinas Interviews W. Brian Arthur of the Santa Fe Institute on Complexity Science and Chaos

    http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=7d17456913&e=55e25a0e3e

Complexity Theory is an emerging field of scientific study that seeks to offer a better framework for understanding dynamic, complex adaptive systems.

Source: www.hiddenforcespod.com (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=6f5545477b&e=55e25a0e3e)

Socinfo2017 ˙˙ 9th International Conference on Social Informatics

    http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=02b9f47fdc&e=55e25a0e3e

We are delighted to welcome the 9th International Conference on Social Informatics (SocInfo 2017) to Oxford, UK, in September 2017.

SocInfo is an interdisciplinary venue for researchers from Computer Science, Informatics, Social Sciences and Management Sciences to share ideas and opinions, and present original research work on studying the interplay between socially-centric platforms and social phenomena.
The ultimate goal of Social Informatics is to create better understanding of socially-centric platforms not just as a technology, but also as a set of social phenomena. To that end, we are inviting interdisciplinary papers, on applying information technology in the study of social phenomena, on applying social concepts in the design of information systems, on applying methods from the social sciences in the study of social computing and information systems, on applying computational algorithms to facilitate the study of social systems and human social dynamics, and on designing information and communication technologies that consider social context.

Source: socinfo2017.oii.ox.ac.uk (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=742e1d842d&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&id=9432a45cf4&e=55e25a0e3e ) and using the "Suggest" button.
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