Exact Community Recovery over Signed Graphs

TL;DR

This paper introduces a novel approach for exact community recovery in signed graphs using a maximum likelihood estimation framework, revealing the importance of treating positive and negative edges differently, and provides an efficient algorithm that achieves near-optimal recovery.

Contribution

It formulates community detection over signed graphs as a regularized MLE problem and proposes a two-stage iterative algorithm that achieves exact recovery at the information-theoretic limit.

Findings

Algorithm recovers communities in nearly-linear time.

Method achieves exact recovery at the information-theoretic limit.

Validated on synthetic and real datasets.

Abstract

Signed graphs encode similarity and dissimilarity relationships among different entities with positive and negative edges. In this paper, we study the problem of community recovery over signed graphs generated by the signed stochastic block model (SSBM) with two equal-sized communities. Our approach is based on the maximum likelihood estimation (MLE) of the SSBM. Unlike many existing approaches, our formulation reveals that the positive and negative edges of a signed graph should be treated unequally. We then propose a simple two-stage iterative algorithm for solving the regularized MLE. It is shown that in the logarithmic degree regime, the proposed algorithm can exactly recover the underlying communities in nearly-linear time at the information-theoretic limit. Numerical results on both synthetic and real data are reported to validate and complement our theoretical developments and demonstrate the efficacy of the proposed method.