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.

References in corpus (6)

• Modularity and community structure in networks
• Uncovering the overlapping community structure of complex networks in nature and society
• Resolution limit in community detection
• Detecting the overlapping and hierarchical community structure of complex networks
• Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
• Community Structure in Jazz

Cited by in corpus (8)

• Community Detection in Complex Networks Using Density-based Clustering Algorithm
• Community detection in bipartite networks using weighted symmetric binary matrix factorization
• Identification of Overlapping Communities via Constrained Egonet Tensor Decomposition
• 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