Spectral Measures of Bipartivity in Complex Networks

TL;DR

This paper introduces spectral measures to quantify bipartivity in complex networks, linking structural properties to functional and community features across various network types.

Contribution

It presents a novel spectral approach to measure bipartivity, connecting network structure with functional and community characteristics.

Findings

Spectral measures effectively quantify bipartivity in networks.

Bipartivity correlates with network efficiency and community structure.

The method applies to diverse network types, including social and biological networks.

Abstract

We introduce a quantitative measure of network bipartivity as a proportion of even to total number of closed walks in the network. Spectral graph theory is used to quantify how close to bipartite a network is and the extent to which individual nodes and edges contribute to the global network bipartivity. It is shown that the bipartivity characterizes the network structure and can be related to the efficiency of semantic or communication networks, trophic interactions in food webs, construction principles in metabolic networks, or communities in social networks.