Combining Random Walks and Nonparametric Bayesian Topic Model for Community Detection

Ruimin Zhu; Wenxin Jiang

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arXiv · 1607.05573

Combining Random Walks and Nonparametric Bayesian Topic Model for Community Detection

Ruimin Zhu·Wenxin Jiang

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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.

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Ruimin Zhu, Wenxin Jiang. 2016-07-19. Combining Random Walks and Nonparametric Bayesian Topic Model for Community Detection. https://arxiv.org/abs/1607.05573

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