Non-Convex Exact Community Recovery in Stochastic Block Model

• URL: http://arxiv.org/abs/2006.15843v4
• Date: Mon, 27 Sep 2021 12:03:04 GMT
• Title: Non-Convex Exact Community Recovery in Stochastic Block Model
• Authors: Peng Wang, Zirui Zhou, Anthony Man-Cho So
• Abstract summary: Community detection in graphs that are generated according to symmetric block models (SBMs) has received much attention lately. We show that in the logarithmic sparsity regime of the problem, with high probability the proposed two-stage method can exactly recover the two communities down to the information-theoretic limit in $mathcalO(nlog2n/loglog n)$ time. We also conduct numerical experiments on both synthetic and real data sets to demonstrate the efficacy of our proposed method and complement our theoretical development.
• Score: 31.221745716673546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Community detection in graphs that are generated according to stochastic block models (SBMs) has received much attention lately. In this paper, we focus on the binary symmetric SBM -- in which a graph of $n$ vertices is randomly generated by first partitioning the vertices into two equal-sized communities and then connecting each pair of vertices with probability that depends on their community memberships -- and study the associated exact community recovery problem. Although the maximum-likelihood formulation of the problem is non-convex and discrete, we propose to tackle it using a popular iterative method called projected power iterations. To ensure fast convergence of the method, we initialize it using a point that is generated by another iterative method called orthogonal iterations, which is a classic method for computing invariant subspaces of a symmetric matrix. We show that in the logarithmic sparsity regime of the problem, with high probability the proposed two-stage method can exactly recover the two communities down to the information-theoretic limit in $\mathcal{O}(n\log^2n/\log\log n)$ time, which is competitive with a host of existing state-of-the-art methods that have the same recovery performance. We also conduct numerical experiments on both synthetic and real data sets to demonstrate the efficacy of our proposed method and complement our theoretical development.

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