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Activity Number: 349 - Contributed Poster Presentations: Section on Statistical Graphics
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Graphics
Abstract #323366
Title: Node-Level Community Detection Within Edge Exchangeable Models for Interaction Processes
Author(s): Yuhua Zhang*
Companies: University of Michigan
Keywords: Community Detection; Edge exchangeable model; sparse network; power-law degree distribution

Scientists are increasingly interested in discovering community structure from modern relational data arising on large-scale social networks. While many methods have been proposed for learning community structure, few account for the fact that these modern networks arise from processes of interactions in the population. We introduce block edge exchangeable models (BEEM) for the study of interaction networks with latent node-level community structure. The block vertex components model (B-VCM) is derived as a canonical example. Several theoretical and practical advantages over traditional vertex-centric approaches are highlighted. In particular, BEEMs allow for sparse degree structure and power-law degree distributions within communities. Our theoretical analysis bounds the misspecification rate of cluster assignments, while supporting simulations show the properties of the network can be recovered. A computationally tractable Gibbs algorithm is derived. We demonstrate the proposed model using post-comment interaction data from Talklife, a large-scale online peer-to-peer support network

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

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