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Mark Handcock, UCLA

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Some New Models for Social Networks
22 March 2016 from 4:00 PM to 5:00 PM
201 Thomas Building
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Mark Handcock

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.

In this talk we will consider some new classes of models that generalize ERGM in different ways. First, we model 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 regression where the outcomes, covariates and relations are socially endogenous. We illustrate this with a new class of latent cluster models and network regression.

Next we introduce a class of models we call Tempered Exponential-family Random Network Models (TERNM). These models remove the degeneracy properties that hamper ERGM and ERNM while retaining there advantages. We show how these models can provide good fits to large networks.

Finally we introduce spatial temporal exponential-family of point processes (STEPP) models to jointly represent the co-evolution of social relations and individual behavior in discrete time.

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