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Activity Number: 495 - Statistical Methods for Networks
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #312368
Title: Matrix Factorization Methods for Community Detection in Dynamic Networks
Author(s): Yan Liu* and Yuguo Chen
Companies: University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
Keywords: Dynamic Networks; Spectral Clustering; Community Detection; Stochastic Blockmodels

We consider the problem of estimating the community memberships of the nodes in dynamic networks, where the memberships are allowed to change over time. We propose two matrix-factorization methods to perform community detection in dynamic networks. The first method is a weighted version of orthogonal linked matrix factorization, and the second method is a co-regularization algorithm. Simulation studies demonstrate that the proposed algorithms outperform existing early fusion and late fusion methods under various settings. Real data examples are also provided to demonstrate the performance of the proposed algorithms.

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

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