Sparse Nonnegative Matrix Factorization for Multiple-Local-Community Detection

Abstract

Local community detection consists of finding a group of nodes closely related to the seeds, a small set of nodes of interest. Such groups of nodes are densely connected or have a high probability of being connected internally than their connections to other clusters in the network. Existing local community detection methods focus on finding either one local community that all seeds are most likely to be in or finding a single community for each of the seeds. However, a seed member usually belongs to multiple local overlapping communities. In this work, we present a novel method of detecting multiple local communities to which a single-seed member belongs. The proposed method consists of three key steps: 1) local sampling with Personalized PageRank (PPR); 2) using the sparseness generated by a sparse nonnegative matrix factorization (SNMF) to estimate the number of communities in the sampled subgraph; and 3) using SNMF soft community membership vectors to assign nodes to communities. The proposed method shows favorable accuracy performance and a good conductance compared with state-of-the-art community detection methods by experiments using a combination of artificial and real-world networks.