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SOCNET  December 2013

SOCNET December 2013

Subject:

Selected Latest Complexity Digest Posts (fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 16 Dec 2013 09:20:55 -0500

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (77 lines)

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

Please, please someone take this curating over from me. Two peeps have 
started but then disappeared.

I just go thru & delete the ones that are less relevant.

Many of you should consider subscribing to Complexity Digest yourselves.

   Barry Wellman
  _______________________________________________________________________

   NetLab                        FRSC                      INSNA Founder
   Faculty of Information (iSchool)                 611 Bissell Building
   140 St. George St.    University of Toronto    Toronto Canada M5S 3G6
   http://www.chass.utoronto.ca/~wellman          twitter: @barrywellman
                  NSA/CSEC: Canadian and American citizen
   NETWORKED:The New Social Operating System. Lee Rainie & Barry Wellman
   MIT Press            http://amzn.to/zXZg39      Print $20  Kindle $16
                  Old/NewCyberTimes http://bit.ly/c8N9V8
   ________________________________________________________________________


---------- Forwarded message ----------
Date: Mon, 16 Dec 2013 07:58:29 -0600
From: Complexity Digest Administration <[log in to unmask]>
To: [log in to unmask]
Subject: [comdig] Latest Complexity Digest Posts

Learn about the latest and greatest related to complex systems research. More at http://comdig.unam.mx

The Hidden Geometry of Complex, Network-Driven Contagion Phenomena

    The global spread of epidemics, rumors, opinions, and innovations are complex, network-driven dynamic processes. The combined multiscale nature and intrinsic heterogeneity of the underlying networks make it difficult to develop an intuitive understanding of these processes, to distinguish relevant from peripheral factors, to predict their time course, and to locate their origin. However, we show that complex spatiotemporal patterns can be reduced to surprisingly simple, homogeneous wave propagation patterns, if conventional geographic distance is replaced by a probabilistically motivated effective distance. In the context of global, air-traffic˙˙mediated epidemics, we show that effective distance reliably predicts disease arrival times. Even if epidemiological parameters are unknown, the method can still deliver relative arrival times. The approach can also identify the spatial origin of spreading processes and successfully be applied to data of the worldwide 2009 H1N1
influenza pandemic and 2003 SARS epidemic.

The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
Dirk Brockmann, Dirk Helbing

Science 13 December 2013:
Vol. 342 no. 6164 pp. 1337-1342
http://dx.doi.org/10.1126/science.1245200

See it on Scoop.it (http://www.scoop.it/t/papers/p/4012653703/2013/12/13/the-hidden-geometry-of-complex-network-driven-contagion-phenomena) , via Papers (http://www.scoop.it/t/papers)


Efficient discovery of overlapping communities in massive networks

    Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately
discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks.

See it on Scoop.it (http://www.scoop.it/t/papers/p/4012403997/2013/12/09/efficient-discovery-of-overlapping-communities-in-massive-networks) , via Papers (http://www.scoop.it/t/papers)

Local Activity Principle:The Cause of Complexity and Symmetry Breaking: Klaus Mainzer, Leon Chua

    The principle of local activity explains the emergence of complex patterns in a homogeneous medium. At first defined in the theory of nonlinear electronic circuits in a mathematically rigorous way, it can be generalized and proven at least for the class of nonlinear reaction˙˙diffusion systems in physics, chemistry, biology, and brain research. Recently, it was realized by memristors for nanoelectronic device applications. In general, the emergence of complex patterns and structures is explained by symmetry breaking in homogeneous media, which is caused by local activity. This book argues that the principle of local activity is really fundamental in science, and can even be identified in quantum cosmology as symmetry breaking of local gauge symmetries generating the complexity of matter and forces in our universe. Applications are considered in economic, financial, and social systems with the emergence of equilibrium states, symmetry breaking at critical points of phase
transitions and risky acting at the edge of chaos.

See it on Scoop.it (http://www.scoop.it/t/cxbooks/p/4012404642/2013/12/09/local-activity-principle-the-cause-of-complexity-and-symmetry-breaking-klaus-mainzer-leon-chua) , via CxBooks (http://www.scoop.it/t/cxbooks)



To manage subscriptions, please go to http://comdig.unam.mx/subscriptions.php

You can contribute to Complexity Digest selecting one of our topics (http://www.scoop.it/u/complexity-digest ) and using the "Suggest" button.
==============================================
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