***** 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 networkÃ¢â^Â¬â^Ä¢s 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 networkÃ¢â^Â¬â^À^Ýaccording observed dataÃ¢â^Â¬â^À^Ýis 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 Ã¢â^Â¬Ë^Ütit-for-tatÃ¢â^Â¬â^Ä¢ is a weak catalyst for the emergence of cooperation, while Ã¢â^Â¬Ë^Üalways cooperateÃ¢â^Â¬â^Ä¢ 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 _______________________________________________________________________ _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.