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SOCNET  May 2008

SOCNET May 2008

Subject:

from Friday's Complexity Digest

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Sun, 11 May 2008 10:37:38 -0400

Content-Type:

TEXT/PLAIN

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (99 lines)

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

 Barry Wellman
 _______________________________________________________________________

  S.D. Clark Professor of Sociology, FRSC              NetLab Director
  Department of Sociology                        University of Toronto
  725 Spadina Avenue, Room 388                  Toronto Canada M5S 2J4
  http://www.chass.utoronto.ca/~wellman            fax:+1-416-978-3963
  Updating history:     http://chass.utoronto.ca/oldnew/cybertimes.php
         Elvis wouldn't be singing "Return to Sender" these days
            **** PLEASE NOTE NEW ADDRESS AND FAX NUMBER ****
 _______________________________________________________________________

These all seem to be takes on the same article or set of articles

Extracting The Structure Of Networks

"Unlike much previous work in this area, Clauset, Moore, and Newman
propose a direct but flexible model of hierarchical structure, which they
apply to networks using the tools of statistical physics and machine
learning. To demonstrate the practical utility of their model, they
analyze networks from three disparate fields: the metabolic network of the
spirochete Treponema pallidum (the bacteria that causes syphilis), a
network of associations between terrorists, and a food web of grassland
species. Even when only half of the connections in these networks were
shown to their algorithm, the researchers found that hierarchical
structure can predict missing connections with an accuracy of up to 80
percent."

Extracting The Structure Of Networks, Roland Piquepaille, 08/05/03, ZDNet

[7] http://blogs.zdnet.com/emergingtech/?p=911

-----------------------

 Networks: Teasing Out The Missing Links , Nature

Excerpts: Focusing on the hierarchical structure inherent in social and
biological networks might provide a smart way to find missing connections
that are not revealed in the raw data - which could be useful in a range
of contexts.

* [8] Networks: Teasing Out The Missing Links, Sid Redner, 08/05/01, DOI:
10.1038/453047a, Nature 453, 47-48

[8] http://www.nature.com/nature/journal/v453/n7191/full/453047a.html

_________________________________________________________________

02.02. Scientists Develop Technique For Extracting Hierarchical Structure
Of
Networks , ScienceDaily

Excerpts: Networks -- used throughout the sciences in the study of
biological, technological, and social complexity -- can often be too
complex to visualize or understand. (...) (...) many real-world networks
can be understood as a hierarchy of modules, where nodes cluster together
to form modules, which themselves cluster into larger modules --
arrangements similar to the organization of sports players into teams,
teams into conferences, and conferences into leagues, for example. This
hierarchical organization, the researchers show, can simultaneously
explain a number of patterns previously discovered in networks, such as
the surprising heterogeneity in the number of connections some nodes have,
or the prevalence of triangles in a network diagram. Their discovery
suggests that hierarchy may, in fact, be a fundamental organizational
principle for complex networks.

* [9] Scientists Develop Technique For Extracting Hierarchical Structure
Of Networks, 08/05/03, ScienceDaily, Santa Fe Institute

[9] http://www.sciencedaily.com/releases/2008/05/080501125414.htm

_________________________________________________________________

02.03. Hierarchical Structure And The Prediction Of Missing Links In
Networks ,
Nature

Excerpts: Networks have in recent years emerged as an invaluable tool for
describing and quantifying complex systems in many branches of science.
(...) We further show that knowledge of hierarchical structure can be used
to predict missing connections in partly known networks with high
accuracy, and for more general network structures than competing
techniques. Taken together, our results suggest that hierarchy is a
central organizing principle of complex networks, capable of offering
insight into many network phenomena.

* [10] Hierarchical Structure And The Prediction Of Missing Links In
Networks, Aaron Clauset, Cristopher Moore, M. E. J. Newman, 08/05/01, DOI:
10.1038/nature06830, Nature 453, 98-101

[10] http://www.nature.com/nature/journal/v453/n7191/full/nature06830.html

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