Overlapping Community Detection in Complex Networks using Symmetric Binary Matrix Factorization

Abstract

Discovering overlapping community structures is a crucial step to understanding the structure and dynamics of many networks. In this paper we develop a symmetric binary matrix factorization model (SBMF) to identify overlapping communities. Our model allows us not only to assign community memberships explicitly to nodes, but also to distinguish outliers from overlapping nodes. In addition, we propose a modified partition density to evaluate the quality of community structures. We use this to determine the most appropriate number of communities. We evaluate our methods using both synthetic benchmarks and real world networks, demonstrating the effectiveness of our approach.

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• Identifying multi-scale communities in networks by asymptotic surprise
• Link community detection through global optimization and the inverse resolution limit of partition density
• Integrating Network Embedding and Community Outlier Detection via Multiclass Graph Description
• Parameterized algorithms for identifying gene co-expression modules via weighted clique decomposition
• On Equivalence of Likelihood Maximization of Stochastic Block Model and Constrained Nonnegative Matrix Factorization