HellRank: A Hellinger-based Centrality Measure for Bipartite Social Networks

TL;DR

HellRank is a novel centrality measure for bipartite social networks based on Hellinger distance, allowing distributed computation and effectively identifying influential nodes without full network knowledge.

Contribution

This paper introduces HellRank, a new Hellinger-based centrality measure for bipartite networks that can be computed locally and correlates with existing metrics.

Findings

HellRank can be computed using only local information.

It shows partial ranking similarity with traditional centrality measures.

Theoretical bounds for Hellinger distance in bipartite networks.

Abstract

Measuring centrality in a social network, especially in bipartite mode, poses several challenges such as requirement of full knowledge of the network topology and lack of properly detection of top-k behavioral representative users. In this paper, to overcome the aforementioned challenging issues, we propose an accurate centrality measure, called HellRank, to identify central nodes in bipartite social networks. HellRank is based on the Hellinger distance between two nodes on the same side of a bipartite network. We theoretically analyze the impact of the Hellinger distance on a bipartite network and find an upper and lower bounds for this distance. The computation of HellRank centrality measure can be distributed by letting each node uses only local information on its immediate neighbors and therefore do not need a central entity to have full knowledge of the network topological structure. We experimentally evaluate performance of the HellRank measure in correlation with other centrality measures on real-world networks. The results show partial ranking similarity between the HellRank and the other conventional metrics according to the Kendall and Spearman rank correlation coefficient.