Generative models for local network community detection
This work addresses the problem of efficiently detecting single communities in large networks for researchers and practitioners, offering a novel probabilistic approach that is incremental in methodology.
The paper tackled local network community detection by developing a method based on generative models, specifically variants of the stochastic block model, to approximate unobserved network parts and find communities around seed nodes. Experiments on real and synthetic datasets showed results comparable to or better than state-of-the-art algorithms.
Local network community detection aims to find a single community in a large network, while inspecting only a small part of that network around a given seed node. This is much cheaper than finding all communities in a network. Most methods for local community detection are formulated as ad-hoc optimization problems. In this work, we instead start from a generative model for networks with community structure. By assuming that the network is uniform, we can approximate the structure of unobserved parts of the network to obtain a method for local community detection. We apply this local approximation technique to two variants of the stochastic block model. To our knowledge, this results in the first local community detection methods based on probabilistic models. Interestingly, in the limit, one of the proposed approximations corresponds to conductance, a popular metric in this field. Experiments on real and synthetic datasets show comparable or improved results compared to state-of-the-art local community detection algorithms.