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SOCNET  January 2010

SOCNET January 2010

Subject:

today's complexity digest

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Tue, 5 Jan 2010 15:45:59 -0500

Content-Type:

TEXT/PLAIN

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (131 lines)

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

 Predictive Analysis for Social Diffusion: The Role of Network Communities, arXiv

Excerpts: The diffusion of information and behaviors over social networks
is of considerable interest in research fields ranging from sociology to
computer science and application domains such as marketing, finance, human
health, and national security. Of particular interest is the possibility
to develop predictive capabilities for social diffusion, for instance
enabling early identification of diffusion processes which are likely to
become "viral" and propagate to a significant fraction of the population.
(...) We explore these hypotheses with case studies involving the
emergence of the Swedish Social Democratic Party at the turn of the 20th
century, the spread of the SARS virus in 2002-2003, and blogging dynamics
associated with real world protest activity. These empirical studies
demonstrate that network community-based diffusion metrics do indeed
possess predictive power, and in fact can be more useful than standard
measures.

* [17] Predictive Analysis for Social Diffusion: The Role of Network
Communities, Richard Colbaugh, Kristin Glass, 2009/12/29, arXiv:
0912.5242

[17] http://arXiv.org/abs/0912.5242

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

11.01. Untangling the interplay between epidemic spreading and
transmission
network dynamic , arXiv

Excerpt: Epidemic spreading of infectious diseases is ubiquitous and has
often considerable impact on public health and economic wealth. The large
variability in spatio-temporal patterns of epidemics prohibits simple
interventions and demands for a detailed analysis of each epidemic with
respect to its infectious agent and the corresponding routes of
transmission. To facilitate this analysis, a mathematical framework is
introduced which links epidemic patterns to the topology and dynamics of
the underlying transmission network.

* [18] Untangling the interplay between epidemic spreading and
transmission network dynamic, Christel Kamp, 2009/12/21, arXiv:0912.4189

[18] http://arXiv.org/abs/0912.4189

_________________________________________________________________

11.02. Robustness of centrality measures under uncertainty: Examining the
role of network topology , Computational & Mathematical Organization
Theory

Abstract: This study investigates the topological form of a network and
its impact on the uncertainty entrenched in descriptive measures computed
from observed social network data, given ubiquitous data-error. We
investigate what influence a network√Ę‚^¬¨‚^ńĘs topology, in conjunction
with the type and amount of error, has on the ability of a measure,
derived from observed data, to correctly approximate the same of the
ground-truth network. By way of a controlled experiment, we reveal the
differing effect that observation error has on measures of centrality and
local clustering across several network topologies: uniform random,
small-world, core-periphery, scale-free, and cellular. Beyond what is
already known about the impact of data uncertainty, we found that the
topology of a social network is, indeed, germane to the accuracy of these
measures. In particular, our experiments show that the accuracy of
identifying the prestigious, or key, actors in a
network√Ę‚^¬¨‚^ņ^›according observed data√Ę‚^¬¨‚^ņ^›is considerably
predisposed by the topology of the ground-truth network.

* [19] Robustness of centrality measures under uncertainty: Examining the
role of network topology, Terrill L. Frantz, Marcelo Cataldo, Kathleen M.
Carley, DOI: 10.1007/s10588-009-9063-5, Computational & Mathematical
Organization Theory Volume 15, Number 4 2009/12

[19] http://dx.doi.org/10.1007/s10588-009-9063-5

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

 Stochastic evolutionary dynamics of direct reciprocity , Proc. R.
Soc. B

Excerpt: Evolutionary game theory is the study of frequency-dependent
selection. The success of an individual depends on the frequencies of
strategies that are used in the population. We propose a new model for
studying evolutionary dynamics in games with a continuous strategy space.
[...] We find that √Ę‚^¬¨ň^‹tit-for-tat√Ę‚^¬¨‚^ńĘ is a weak catalyst for
the emergence of cooperation, while √Ę‚^¬¨ň^‹always cooperate√Ę‚^¬¨‚^ńĘ is
a strong catalyst for the emergence of defection. Our analysis leads to a
new understanding of the optimal level of forgiveness that is needed for
the evolution of cooperation under direct reciprocity.

* [21] Stochastic evolutionary dynamics of direct reciprocity, Imhof LA ,
Nowak MA, February 2010, DOI: 10.1098/rspb.2009.1171, Proc. R. Soc. B 277,
n 1680,pp. 463-468 * Contributed by [22] Segismundo

[21] http://dx.doi.org/10.1098/rspb.2009.1171

---------------------------
Joint evolution of multiple social traits: a kin selection analysis ,
Proc. R. Soc. B

Excerpt: General models of the evolution of cooperation, altruism and
other social behaviours have focused almost entirely on single traits,
whereas it is clear that social traits commonly interact. We develop a
general kin- selection framework for the evolution of social behaviours in
multiple dimensions. We show that whenever there are interactions among
social traits new behaviours can emerge that are not predicted by
one-dimensional analyses.

* [23] Joint evolution of multiple social traits: a kin selection
analysis, Brown SP , Taylor PD, February 2010, DOI:
10.1098/rspb.2009.1480, Proc. R. Soc. B 277, n 1680,pp. 415-422 [23]
http://dx.doi.org/10.1098/rspb.2009.1480




 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
 _______________________________________________________________________

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