Modeling Heterogeneous Networks using Sender-Receiver Finite Mixture Exponential Random Graph Models

Teague Henry, UNC - Chapel Hill, Quantitative Psychology
Model-based inference on networks is made difficult by the interdependencies inherent in network data. The majority of model based inferential techniques for use on network data make an assumption of homogeneity, in that the data generating mechanism is identical for all edges and nodes within the network. However, failure to model potential heterogeneities can have wide-ranging effects on model misfit, both in terms of bias and efficiency, and these effects are made all the more problematic by the interdependency of the network. In this talk, we discuss heterogeneity in the framework of exponential random graph models, examine the consequences of leaving heterogeneity unmodelled, discuss the current modeling techniques to handle heterogeneity, and finally, introduce a novel method to handle heterogeneity in the form of the Sender-Receiver Finite Mixture Exponential Random Graph Model (SRFM-ERGM).
January, 30 2017 | 12:45 p.m. - 2:00 p.m. | Gross Hall 230E

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