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SOCNET  February 2010

SOCNET February 2010

Subject:

from yesterday's Complexity Digest, 12Feb10

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Sat, 13 Feb 2010 09:26:36 -0500

Content-Type:

TEXT/PLAIN

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (189 lines)

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

either lotsa stuff here, or I was in an accepting mood
Props to "Segismundo" who contributed most of the abstracts I copied.

 Barry Wellman
 _______________________________________________________________________

  S.D. Clark Professor of Sociology, FRSC               NetLab Director
  Department of Sociology                  725 Spadina Avenue, Room 388
  University of Toronto   Toronto Canada M5S 2J4   twitter:barrywellman
  http://www.chass.utoronto.ca/~wellman             fax:+1-416-978-3963
  Updating history:      http://chass.utoronto.ca/oldnew/cybertimes.php
 _______________________________________________________________________

 Community detection in graphs , Physics Reports

Excerpt: One of the most relevant features of graphs representing real
systems is community structure, or clustering [...] We will attempt a
thorough exposition of the topic, from the definition of the main elements
of the problem, to the presentation of most methods developed, [...]

* [10] Community detection in graphs, Fortunato S, February 2010, DOI:
10.1016/j.physrep.2009.11.002, Physics Reports 486, 3-5: 75-174

[10] http://dx.doi.org/10.1016/j.physrep.2009.11.002

-------------------------
 A Spatial Approach to Network Generation for Three Properties:
Degree
Distribution, Clustering Coefficient and Degree Assortativity , JASSS

Excerpt: We introduce a spatially constructed algorithm that generates
networks with constrained but arbitrary degree distribution, clustering
coefficient and assortativity. Both a general approach and specific
implementation are presented. The specific implementation is validated and
used to generate networks with a constrained but broad range of property
values.

* [12] A Spatial Approach to Network Generation for Three Properties:
Degree Distribution, Clustering Coefficient and Degree Assortativity,
Badham J , Stocker R, January 2010, Journal of Artificial Societies and
Social Simulation 13 (1) 11

[12] http://jasss.soc.surrey.ac.uk/13/1/11.html

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

Competition drives cooperation among closely related sperm of deer
mice ,
Nature

Summary: Sperm can increase their swimming velocity and gain a competitive
advantage over sperm from another male by forming cooperative groups, such
that selection should favour cooperation of the most closely related
sperm.  Sperm of deer mice are now shown to aggregate more often with
conspecific than heterospecific sperm, in accordance with this theory,
whereas in a monogamous species lacking sperm competition, sperm
indiscriminately group with unrelated conspecific sperm.

* [14] Competition drives cooperation among closely related sperm of deer
mice, Heidi S. Fisher & Hopi E. Hoekstra, 2010/02/11, DOI: 10.1038/
nature08736, Nature 463, 801-803

[14] http://dx.doi.org/10.1038/nature08736

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

 The common patterns of nature , Journal of Evolutionary Biology

Excerpt: We typically observe large-scale outcomes that arise from the
interactions of many hidden, small-scale processes. Examples include age
of disease onset, rates of amino acid substitutions and composition of
ecological communities. The macroscopic patterns in each problem often
vary around a characteristic shape that can be generated by neutral
processes. A neutral generative model assumes that each microscopic
process follows unbiased or random stochastic fluctuations: random
connections of network nodes;  amino acid substitutions with no effect on
fitness; species that arise or disappear from communities randomly. These
neutral generative models often match common patterns of nature. In this
paper, I present the theoretical background by which we can understand why
these neutral generative models are so successful.(...) This framework
shows that each neutral generative model is a special case that helps to
discover a particular set of informational constraints; those
informational constraints define a much wider domain of non-neutral
generative processes that attract to the same neutral pattern.

* [15] The common patterns of nature, S. A. Frank, 2009/06/18, DOI:
10.1111/j.1420-9101.2009.01775.x, Journal of Evolutionary Biology Volume
22 Issue 8, Pages 1563 - 1585
[15] http://dx.doi.org/10.1111/j.1420-9101.2009.01775.x

_________________________________________________________________

11. Complex networks: new trends for the analysis of brain
connectivity , arXiv

Abstract: Today, the human brain can be studied as a whole.
Electroencephalography, magnetoencephalography, or functional magnetic
resonance imaging techniques provide functional connectivity patterns
between different brain areas, and during different pathological and
cognitive neuro- dynamical states. In this Tutorial we review novel
complex networks approaches to unveil how brain networks can efficiently
manage local processing and global integration for the transfer of
information, while being at the same time capable of adapting to satisfy
changing neural demands.

* [16] Complex networks: new trends for the analysis of brain
connectivity, Mario Chavez, Miguel Valencia, Vito Latora and Jacques
Martinerie, 2010/02/3, arXiv:1002.0697

[16] http://arXiv.org/abs/1002.0697

_________________________________________________________________

12. A Methodology for Complex Social Simulations , JASSS

Excerpt: This paper proposes a methodology for complex social simulations
-particularly inter- and multi-disciplinary socio-natural systems with
multi-level architecture- based on a succession of models akin to but
distinct from the late Imre Lakatos' notion of a 'research programme'. The
proposed methodology is illustrated through examples...

* [17] A Methodology for Complex Social Simulations, Cioffi-Revilla C,
January 2010, Journal of Artificial Societies and Social Simulation 13 (1)
7 [17] http://jasss.soc.surrey.ac.uk/13/1/7.html

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

 Altruism in Forest Chimpanzees: The Case of Adoption , PLoS ONE

Excerpt: In recent years, extended altruism towards unrelated group
members has been proposed to be a unique characteristic of human
societies. [...] These observations reveal that, under the appropriate
socio-ecologic conditions, chimpanzees do care for the welfare of other
unrelated group members and that altruism is more extensive in wild
populations than was suggested by captive studies.

* [29] Altruism in Forest Chimpanzees: The Case of Adoption, Boesch C ,
Bol√^√¬© C , Eckhardt N , Boesch H, January 27, 2010, DOI:
10.1371/journal.pone.  0008901, PLoS ONE 5(1): e8901

[29] http://dx.doi.org/10.1371/journal.pone.0008901

--------------------
 Evolution of cooperation in rotating indivisible goods game , Journal of
Theoretical Biology

Excerpt: Collective behavior is theoretically and experimentally studied
through a public goods game in which players contribute resources or
efforts to produce goods (or pool), which are then divided equally among
all players regardless of the amount of their contribution. However, if
goods are indivisible, only one player can receive the goods. In this
case, the problem is how to distribute indivisible goods, and here
therefore we propose a new game, namely the √Ę‚^¬¨Ň^”rotating indivisible
goods game.√Ę‚^¬¨¬^› In this game, the goods are not divided but
distributed by regular rotation. We elucidate mechanisms that sustain
cooperation in rotating indivisible goods games by means of evolutionary
simulations.

* [33] Evolution of cooperation in rotating indivisible goods game, Koike
S , Nakamaru M , Tsujimoto M, January 2010, DOI: 10.1016/j.jtbi.
2009.12.030, Journal of Theoretical Biology, in Press

[33] http://dx.doi.org/10.1016/j.jtbi.2009.12.030
-------------------

Can Power-Law Scaling and Neuronal Avalanches Arise from Stochastic
Dynamics? , PLoS ONE

Excerpt: The presence of self-organized criticality in biology is often
evidenced by a power-law scaling of event size distributions, which can be
measured by linear regression on logarithmic axes. We show here that such
a procedure does not necessarily mean that the system exhibits self-
organized criticality. [...] We conclude that logarithmic representations
can lead to spurious power-law scaling induced by the stochastic nature of
the phenomenon. This apparent power-law scaling does not constitute a
proof of self- organized criticality, which should be demonstrated by more
stringent statistical tests.

* [45] Can Power-Law Scaling and Neuronal Avalanches Arise from Stochastic
Dynamics?, Touboul J , Destexhe A, February 2010, DOI:
10.1371/journal.pone.0008982, PLoS ONE 5(2): e8982

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