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SOCNET  March 2017

SOCNET March 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, 27 Mar 2017 09:46:36 -0400

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (89 lines)

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

spring is almost coming to the Free North
selections below
   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, 27 Mar 2017 12:03:53 +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=2982f5c650&e=55e25a0e3e



Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic

    http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=531215bc99&e=55e25a0e3e

Epidemics can spread across large regions becoming pandemics by flowing along transportation and social networks. Two network attributes, transitivity (when a node is connected to two other nodes that are also directly connected between them) and centrality (the number and intensity of connections with the other nodes in the network), are widely associated with the dynamics of transmission of pathogens. Here we investigate how network centrality and transitivity influence vulnerability to diseases of human populations by examining one of the most devastating pandemic in human history, the fourteenth century plague pandemic called Black Death. We found that, after controlling for the city spatial location and the disease arrival time, cities with higher values of both centrality and transitivity were more severely affected by the plague. A simulation study indicates that this association was due to central cities with high transitivity undergo more exogenous re-infections. Our
study provides an easy method to identify hotspots in epidemic networks. Focusing our effort in those vulnerable nodes may save time and resources by improving our ability of controlling deadly epidemics.


Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic
José M. Gómez & Miguel Verdú
Scientific Reports 7, Article number: 43467 (2017)
doi:10.1038/srep43467

Source: www.nature.com (http://unam.us4.list-manage2.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=7874833396&e=55e25a0e3e)




Big data analyses reveal patterns and drivers of the movements of southern elephant seals

    http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=99e02bae6a&e=55e25a0e3e

The growing number of large databases of animal tracking provides an opportunity for analyses of movement patterns at the scales of populations and even species. We used analytical approaches, developed to cope with ˙˙big data˙˙, that require no ˙˙a priori˙˙ assumptions about the behaviour of the target agents, to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina) in the Southern Ocean, that was comprised of >500,000 location estimates collected over more than a decade. Our analyses showed that the displacements of these seals were described by a truncated power law distribution across several spatial and temporal scales, with a clear signature of directed movement. This pattern was evident when analysing the aggregated tracks despite a wide diversity of individual trajectories. We also identified marine provinces that described the migratory and foraging habitats of these seals. Our analysis provides evidence for the presence of intrinsic drivers of
movement, such as memory, that cannot be detected using common models of movement behaviour. These results highlight the potential for ˙˙big data˙˙ techniques to provide new insights into movement behaviour when applied to large datasets of animal tracking.


Big data analyses reveal patterns and drivers of the movements of southern elephant seals
Jorge P. Rodríguez, Juan Fernández-Gracia, Michele Thums, Mark A. Hindell, Ana M. M. Sequeira, Mark G. Meekan, Daniel P. Costa, Christophe Guinet, Robert G. Harcourt, Clive R. McMahon, Monica Muelbert, Carlos M. Duarte & Víctor M. Eguíluz
Scientific Reports 7, Article number: 112 (2017)
doi:10.1038/s41598-017-00165-0

Source: www.nature.com (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=70edf43c60&e=55e25a0e3e)



Quantifying Synergistic Information Using Intermediate Stochastic Variables

    http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=306cbebd28&e=55e25a0e3e

Quantifying synergy among stochastic variables is an important open problem in information theory. Information synergy occurs when multiple sources together predict an outcome variable better than the sum of single-source predictions. It is an essential phenomenon in biology such as in neuronal networks and cellular regulatory processes, where different information flows integrate to produce a single response, but also in social cooperation processes as well as in statistical inference tasks in machine learning. Here we propose a metric of synergistic entropy and synergistic information from first principles. The proposed measure relies on so-called synergistic random variables (SRVs) which are constructed to have zero mutual information about individual source variables but non-zero mutual information about the complete set of source variables. We prove several basic and desired properties of our measure, including bounds and additivity properties. In addition, we prove
several important consequences of our measure, including the fact that different types of synergistic information may co-exist between the same sets of variables. A numerical implementation is provided, which we use to demonstrate that synergy is associated with resilience to noise. Our measure may be a marked step forward in the study of multivariate information theory and its numerous applications


Quantifying Synergistic Information Using Intermediate Stochastic Variables
Rick Quax, Omri Har-Shemesh and Peter M. A. Sloot

Entropy 2017, 19(2), 85; doi:10.3390/e19020085

Source: www.mdpi.com (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=5849f5cbda&e=55e25a0e3e)


You can contribute to Complexity Digest selecting one of our topics (http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=64508ebb4e&e=55e25a0e3e ) and using the "Suggest" button.
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