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