Non-Convex Joint Community Detection and Group Synchronization via Generalized Power Method

• URL: http://arxiv.org/abs/2112.14204v1
• Date: Tue, 28 Dec 2021 16:17:51 GMT
• Title: Non-Convex Joint Community Detection and Group Synchronization via Generalized Power Method
• Authors: Sijin Chen, Xiwei Cheng, Anthony Man-Cho So
• Abstract summary: This paper proposes a Generalized Power Method (GPM) to tackle the problem of community detection and group synchronization simultaneously. It is shown that the algorithm is able to exactly recover the ground truth in $O(n2n$) $log2n$) in a time of $3.5$.
• Score: 23.62113376505929
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a Generalized Power Method (GPM) to tackle the problem of community detection and group synchronization simultaneously in a direct non-convex manner. Under the stochastic group block model (SGBM), theoretical analysis indicates that the algorithm is able to exactly recover the ground truth in $O(n\log^2n)$ time, sharply outperforming the benchmark method of semidefinite programming (SDP) in $O(n^{3.5})$ time. Moreover, a lower bound of parameters is given as a necessary condition for exact recovery of GPM. The new bound breaches the information-theoretic threshold for pure community detection under the stochastic block model (SBM), thus demonstrating the superiority of our simultaneous optimization algorithm over the trivial two-stage method which performs the two tasks in succession. We also conduct numerical experiments on GPM and SDP to evidence and complement our theoretical analysis.

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