"Exponential-family Random Network Models for Social Networks" (Next Week at the Statistics Seminar)

November 8, 2012
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(This article was originally published at Three-Toed Sloth , and syndicated at StatsBlogs.)

Attention conservation notice: Only of interest if you (1) care about statistical models of networks, and (2) will be in Pittsburgh on Monday.

Constant readers will recall that I have often in the past boosted Mark Handcock's work on comparing distributions, measuring trends in inequality, doing sensible inference with power laws, and modeling network structure statistically. I am thus extremely happy to announce next week's seminar:

Mark Handcock, "Exponential-family Random Network Models for Social Networks"
Abstract: Random graphs, where the connections between nodes are considered random variables, have wide applicability in the social sciences. Exponential-family Random Graph Models (ERGM) have shown themselves to be a useful class of models for representing complex social phenomena.
We generalize ERGM by also modeling the attributes of the social actors as random variates, thus creating a random model of both the relational and individual data, which we call Exponential-family Random Network Models (ERNM).
This provides a framework for expanded analysis of network processes, including a new formulation for network progression, where the outcomes, covariates and relations are socially endogenous. In this talk, we focus on a new class of latent cluster models and network regression.
(Joint work with Ian M. Fellows.)
Time and place: 4--5 pm on Monday, 12 November 2012, in the Adamson Wing (Room 136) of Baker Hall

As always, the talk is free and open to the public.

Networks; Enigmas of Chance



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