Combining Random Walks and Nonparametric Bayesian Topic Model for Community Detection

TL;DR

This paper proposes RW-HDP, a novel probabilistic model combining random walks and Hierarchical Dirichlet Process for community detection, automatically determining the number of communities with efficient inference.

Contribution

Introduces RW-HDP, a new nonparametric Bayesian model that uses random walks and Hierarchical Dirichlet Process for community detection and automatic community number determination.

Findings

Effective community detection without predefining number of communities

Utilizes stochastic variational inference for efficiency

Can be extended to online learning algorithms

Abstract

Community detection has been an active research area for decades. Among all probabilistic models, Stochastic Block Model has been the most popular one. This paper introduces a novel probabilistic model: RW-HDP, based on random walks and Hierarchical Dirichlet Process, for community extraction. In RW-HDP, random walks conducted in a social network are treated as documents; nodes are treated as words. By using Hierarchical Dirichlet Process, a nonparametric Bayesian model, we are not only able to cluster nodes into different communities, but also determine the number of communities automatically. We use Stochastic Variational Inference for our model inference, which makes our method time efficient and can be easily extended to an online learning algorithm.