Two-sample Test of Community Memberships of Weighted Stochastic Block Models

Abstract

Suppose two networks are observed for the same set of nodes, where each network is assumed to be generated from a weighted stochastic block model. This paper considers the problem of testing whether the community memberships of the two networks are the same. A test statistic based on singular subspace distance is developed. Under the weighted stochastic block models with dense graphs, the limiting distribution of the proposed test statistic is developed. Simulation results show that the test has correct empirical type 1 errors under the dense graphs. The test also behaves as expected in empirical power, showing gradual changes when the intra-block and inter-block distributions are close and achieving 1 when the two distributions are not so close, where the closeness of the two distributions is characterized by Renyi divergence of order 1/2. The Enron email networks are used to demonstrate the proposed test.

53 pages, 2 figures, 4 tables

References in corpus (7)

• Stochastic blockmodels and community structure in networks
• Concentration of the adjacency matrix and of the Laplacian in random graphs with independent edges
• The geometry of kernelized spectral clustering
• Optimal hypothesis testing for stochastic block models with growing degrees
• Information-theoretic bounds for exact recovery in weighted stochastic block models using the Renyi divergence
• Two-Sample Tests for Large Random Graphs Using Network Statistics
• Asymptotically efficient estimators for stochastic blockmodels: the naive MLE, the rank-constrained MLE, and the spectral

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