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B0737
Title: Mixed membership stochastic blockmodels for heterogeneous networks Authors:  Yuguo Chen - University of Illinois at Urbana-Champaign (United States) [presenting]
Abstract: Heterogeneous networks are useful for modeling complex systems that consist of different types of objects. We formulate a heterogeneous version of the mixed membership stochastic blockmodel to accommodate heterogeneity in the data and the content dependent property of the pairwise relationship. We also apply a variational algorithm for posterior inference. The proposed procedure is shown to be consistent for community detection under mixed membership stochastic blockmodels for heterogeneous networks. We demonstrate the advantage of the proposed method in modeling overlapping communities and multiple memberships through simulation studies and applications to a real data set.