Community detection for weighted bipartite networks

Abstract

The bipartite network appears in various areas, such as biology, sociology, physiology, and computer science. \cite{rohe2016co} proposed Stochastic co-Blockmodel (ScBM) as a tool for detecting community structure of binary bipartite graph data in network studies. However, ScBM completely ignores edge weight and is unable to explain the block structure of a weighted bipartite network. Here, to model a weighted bipartite network, we introduce a Bipartite Distribution-Free model by releasing ScBM's distribution restriction. We also build an extension of the proposed model by considering the variation of node degree. Our models do not require a specific distribution on generating elements of the adjacency matrix but only a block structure on the expected adjacency matrix. Spectral algorithms with theoretical guarantees on the consistent estimation of node labels are presented to identify communities. Our proposed methods are illustrated by simulated and empirical examples.

27pages

References in corpus (8)

• Modularity and community structure in networks
• Comparing community structure identification
• Stochastic blockmodels and community structure in networks
• A Comprehensive Survey on Community Detection with Deep Learning
• Learning Latent Block Structure in Weighted Networks
• Evaluating Local Community Methods in Networks
• Nonparametric weighted stochastic block models
• Robust and computationally feasible community detection in the presence of arbitrary outlier nodes

Cited by in corpus (3)

• Community detection by spectral methods in multi-layer networks
• Mixed membership estimation for categorical data with weighted responses
• Overlapping community detection in weighted networks