Exploiting the Structure of Bipartite Graphs for Algebraic and Spectral Graph Theory Applications

TL;DR

This paper extends algebraic graph analysis methods to bipartite networks, enabling improved clustering, visualization, link prediction, and bipartivity measurement in two-mode networks across various fields.

Contribution

It introduces modifications of algebraic graph theory techniques specifically for bipartite networks, filling a gap in existing network analysis literature.

Findings

Methods for clustering, visualization, and link prediction adapted for bipartite graphs.

New algebraic measures for bipartivity in near-bipartite graphs.

Applicability demonstrated across diverse real-world bipartite networks.

Abstract

In this article, we extend several algebraic graph analysis methods to bipartite networks. In various areas of science, engineering and commerce, many types of information can be represented as networks, and thus the discipline of network analysis plays an important role in these domains. A powerful and widespread class of network analysis methods is based on algebraic graph theory, i.e., representing graphs as square adjacency matrices. However, many networks are of a very specific form that clashes with that representation: They are bipartite. That is, they consist of two node types, with each edge connecting a node of one type with a node of the other type. Examples of bipartite networks (also called \emph{two-mode networks}) are persons and the social groups they belong to, musical artists and the musical genres they play, and text documents and the words they contain. In fact, any type of feature that can be represented by a categorical variable can be interpreted as a bipartite network. Although bipartite networks are widespread, most literature in the area of network analysis focuses on unipartite networks, i.e., those networks with only a single type of node. The purpose of this article is to extend a selection of important algebraic network analysis methods to bipartite networks, showing that many methods from algebraic graph theory can be applied to bipartite networks with only minor modifications. We show methods for clustering, visualization and link prediction. Additionally, we introduce new algebraic methods for measuring the bipartivity in near-bipartite graphs.