Scalable Inference Algorithms for Clustering Large Networks
نویسندگان
چکیده
Clustering is an important task in network analysis, with applications in fields such as biology and the social sciences. We present a novel inference algorithm for the Stochastic Block Model (SBM), a well known network clustering model. Previous inference in this model typically utilizes Markov Chain Monte Carlo or Variational Bayes, but our method is the first to utilize Stochastic Variational Inference to allow the SBM to scale to massive networks. We derive both Variational Bayes and Stochastic Variational Inference algorithms for the SBM, and empirically demonstrate the superior speed and accuracy of Stochastic Variational Inference on synthetic and real networks.
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