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