The clustering coefficient and community structure of bipartite networks

TL;DR

This paper introduces a modified clustering coefficient for bipartite networks and an edge-clustering coefficient to better detect community structures, demonstrating improved characterization over traditional methods.

Contribution

It proposes a new clustering coefficient specific to bipartite networks and an edge-clustering coefficient for community detection, enhancing analysis of bipartite data.

Findings

Modified clustering coefficient better characterizes bipartite networks.

Edge-clustering coefficient effectively detects community structures.

Comparison shows the new measures outperform traditional definitions.

Abstract

Many real-world networks display a natural bipartite structure. It is necessary and important to study the bipartite networks by using the bipartite structure of the data. Here we propose a modification of the clustering coefficient given by the fraction of cycles with size four in bipartite networks. Then we compare the two definitions in a special graph, and the results show that the modification one is better to character the network. Next we define a edge-clustering coefficient of bipartite networks to detect the community structure in original bipartite networks.