Fast Network Community Detection with Profile-Pseudo Likelihood Methods
The stochastic block model is one of the most studied network models for community detection. It is known that most algorithms proposed for fitting the stochastic block model likelihood function cannot scale to large-scale networks. One prominent work that overcomes this computational challenge is Amini et al. (2013), which proposed a fast pseudo-likelihood approach for fitting stochastic block models to large sparse networks. However, this approach does not have a convergence guarantee. In this talk, we present a novel likelihood based approach that decouples row and column labels in the likelihood function, which enables a fast alternating maximization; the new method is computationally efficient and has provable convergence guarantee. We also show that the proposed method provides strongly consistent estimates of the communities in a stochastic block model. As demonstrated in simulation studies, the proposed method outperforms the pseudo-likelihood approach in terms of both estimation accuracy and computation efficiency, especially for large sparse networks. We further consider extensions of the proposed method to handle networks with degree heterogeneity and bipartite properties. This is joint work with Jiangzhou Wang, Jingfei Zhang, Binghui Liu, and Jianhua Guo.