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Activity Number: 544 - Dynamic Graphical Models and Networks with Applications
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: International Indian Statistical Association
Abstract #300192
Title: Mixed Membership Stochastic Blockmodels for Heterogeneous Networks
Author(s): Yuguo Chen*
Companies: University of Illinois at Urbana-Champaign
Keywords: Clustering; Community detection; Heterogeneous network; Mixed membership model; Stochastic blockmodel; Variational algorithm

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 advantage of the proposed method in modeling overlapping communities and multiple memberships is demonstrated through simulation studies and applications to real data.

Authors who are presenting talks have a * after their name.

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