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Fast Network Community Detection with Profile-Pseudo Likelihood Methods

发布时间：2021-06-15 作者： 浏览次数：
Speaker: 张菁菲 DateTime: 2021年6月18日（周五）上午9:30
Brief Introduction to Speaker:

张菁菲，迈阿密大学。

 Place: 腾讯会议（会议号请联系胡建伟老师获?。?/td>
Abstract:The stochastic block model is one of the most studied network models for community detection and fitting its likelihood function on large-scale networks is known to be challenging. 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 convergence guarantee, and may not be well suited for small and medium scale networks. In this article, we propose a novel likelihood-based approach that decouples row and column labels in the likelihood function, enabling a fast alternating maximization. This new method is computationally efficient, performs well for both small- and large-scale networks, and has provable convergence guarantee. We show that our method provides strongly consistent estimates of communities in a stochastic block model.
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