Community estimation in general multilayer stochastic block models

Tue May 18th 2021, 4:30pm
Jing Lei, Carnegie Mellon University

Abstract:   We consider the problem of estimating common community structures in multi-layer stochastic block models, where each single layer may not have sufficient signal strength to recover the full community structure. Two estimators are developed to accommodate general connectivity patterns: a least square approach and a bias-corrected spectral approach. The analyses of these estimators involve some new matrix concentration inequalities. The performance of our method and the necessity of bias removal is demonstrated in synthetic data and in microarray analysis about gene co-expression networks. Some open problems will be briefly discussed.

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