title: 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 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.
authors.affiliation: School of Statistics, Central University of Finance and Economics, Haidian District, Beijing 100081, China. zhyuanzh@gmail.com
authors.forename: Zhong-Yuan
authors.initials: ZY
authors.lastname: Zhang
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
authors.forename: Yong-Yeol
authors.initials: YY
authors.lastname: Ahn
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D012944:Social Support