Community detection in bipartite networks using weighted symmetric binary matrix factorization

TL;DR

This paper introduces weighted symmetric binary matrix factorization (wSBMF), a novel method for detecting overlapping communities in bipartite networks that accounts for different types of missing edges and incorporates prior knowledge.

Contribution

The paper presents a new wSBMF framework that improves community detection in bipartite networks by handling missing edges, explicitly assigning memberships, and incorporating prior information.

Findings

Effective in synthetic and real-world networks

Accurately distinguishes outliers from overlapping nodes

Identifies optimal number of communities using generalized partition density

Abstract

In this paper we propose weighted symmetric binary matrix factorization (wSBMF) framework to detect overlapping communities in bipartite networks, which describe relationships between two types of nodes. Our method improves performance by recognizing the distinction between two types of missing edges---ones among the nodes in each node type and the others between two node types. Our method can also explicitly assign community membership and distinguish outliers from overlapping nodes, as well as incorporating existing knowledge on the network. We propose a generalized partition density for bipartite networks as a quality function, which identifies the most appropriate number of communities. The experimental results on both synthetic and real-world networks demonstrate the effectiveness of our method.