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Activity Number: 316 - Emerging Advances of Innovative Computational Skills with Unconventional Likelihoods
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #300073
Title: Community Detection with Dependent Connectivity
Author(s): Yubai Yuan and Annie Qu*
Companies: University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
Keywords: stochastic block model; Bahadur Representation; high-order approximation; variational EM; product trading network

We propose a new community detection approach to utilize the dependence of network connectivity. One of the most popular probabilistic models for fitting community structure is the stochastic block model (SBM). However, the SBM is not able to fully capture the dependence among edges from the same community. Various SBM approaches using the random effects are proposed to incorporate correlation among edges. However, this mainly designs for the exchangeable dependence structure. In this talk, we illustrate to approximate the true likelihood using the Bahadur representation which allows us to utilize the correlation information among edges within communities. The proposed method provides greater flexibility in handling different types of within-community dependence structure. In addition, the proposed algorithm does not involve specifying the likelihood function which could be intractable in community detection. This is joint work with Yubai Yuan.

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

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