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   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
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   NetLab Network                 FRSC                      INSNA Founder
   Distinguished Visiting Scholar   Social Media Lab   Ryerson University
   Distinguished Senior Advisor     	     University Learning Academy
   http://www.chass.utoronto.ca/~wellman           twitter: @barrywellman
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman
                        http://amzn.to/zXZg39
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---------- Forwarded message ----------
Date: Mon, 11 Sep 2017 11:04:17 +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=a8543b0102&e=55e25a0e3e



Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

    Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node˙˙s concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.


Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks
Tomokatsu Onaga, James P. Gleeson, and Naoki Masuda
Phys. Rev. Lett. 119, 108301

Source: journals.aps.org (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=4514c87e49&e=55e25a0e3e)



Individuality drives collective behavior of schooling fish

    http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=9611c2f181&e=55e25a0e3e

New research sheds light on how "animal personalities" - inter-individual differences in animal behaviour - can drive the collective behaviour and functioning of animal groups such as schools of fish, including their cohesion

Source: phys.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=01bfe56d80&e=55e25a0e3e)



Generative Models for Network Neuroscience: Prospects and Promise

    Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and to identify principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modeling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, utility in intuiting mechanisms, and a short history on their use in network science broadly. We then discuss generative models in practice and application, paying particular attention to
the critical need for cross-validation. Next, we review generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including \emph{C. elegans}, \emph{Drosophila}, mouse, rat, cat, macaque, and human. We offer a careful treatment of a few relevant distinctions, including differences between generative models and null models, sufficiency and redundancy, inferring and claiming mechanism, and functional and structural connectivity. We close with a discussion of future directions, outlining exciting frontiers both in empirical data collection efforts as well as in method and theory development that, together, further the utility of the generative network modeling approach for network neuroscience.


Generative Models for Network Neuroscience: Prospects and Promise
Richard F. Betzel, Danielle S. Bassett

Source: arxiv.org (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=3edb727c69&e=55e25a0e3e)


NERCCS 2018: First Northeast Regional Conference on Complex Systems

    NERCCS 2018: The First Northeast Regional Conference on Complex Systems aims to establish a venue of interdisciplinary scholarly exchange for complex systems researchers in the Northeast U.S. region to share their research outcomes through presentations and post-conference online publications, network with their peers in the region, and promote inter-campus collaboration and the growth of the research community.

NERCCS will particularly focus on facilitating the professional growth of early career faculty, postdocs, and students in the region who have only limited resources but will likely play a leading role in the field of complex systems science and engineering in the coming years.

The conference will be held in the Innovative Technologies Complex at Binghamton University, which is within driving distance from all major urban areas in the U.S. Northeast region.

Source: coco.binghamton.edu (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=cc75c5d5f9&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=7cd34ed73e&e=55e25a0e3e ) and using the "Suggest" button.
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