A0575
Title: Consistent Bayesian community detection for assortative networks
Authors: Sheng Jiang - University of California Santa Cruz (United States) [presenting]
Surya Tokdar - Duke University (United States)
Abstract: Stochastic Block Models (SBMs) are a fundamental tool for community detection in network analysis. But little theoretical work exists on the statistical performance of Bayesian SBMs, especially when the community count is unknown. This paper studies weakly assortative SBMs whose members of the same community are more likely to connect with one another than with members from other communities. The weak assortativity constraint is embedded within an otherwise weak prior, and under mild regularity conditions, the resulting posterior distribution is shown to concentrate on the true community count and membership allocation as the network size grows to infinity. An efficient Gibbs sampler is developed to sample from the posterior distribution. Finite sample properties are examined via simulation studies in which the proposed method offers competitive estimation accuracy relative to existing methods under a variety of challenging scenarios.
