Identifying Influential Nodes in Bipartite Networks Using the Clustering Coefficient

TL;DR

This paper introduces a bipartite clustering coefficient that effectively identifies influential nodes across communities in bipartite networks, addressing limitations of traditional centrality measures in community-structured networks.

Contribution

It proposes a novel bipartite clustering coefficient that captures community structures to better identify influential nodes in bipartite networks.

Findings

The new coefficient outperforms traditional measures in community-rich networks.

It successfully identifies influential nodes across different communities.

The method enhances understanding of node influence in complex bipartite structures.

Abstract

The identification of influential nodes in complex network can be very challenging. If the network has a community structure, centrality measures may fail to identify the complete set of influential nodes, as the hubs and other central nodes of the network may lie inside only one community. Here we define a bipartite clustering coefficient that, by taking differently structured clusters into account, can find important nodes across communities.