Lobby index as a network centrality measure

TL;DR

This paper introduces the lobby index (l-index) as a local network centrality measure, demonstrating its effectiveness and computational efficiency in ranking nodes within biological and linguistic networks.

Contribution

The study proposes the l-index as a novel local centrality measure that outperforms degree and Eigenvector centralities in ranking tasks while being computationally efficient.

Findings

L-index correlates well with degree and Eigenvector centralities.

L-index has poor correlation with betweenness centrality.

L-index provides better ranking results than degree and Eigenvector measures.

Abstract

We study the lobby index (l-index for short) as a local node centrality measure for complex networks. The l-inde is compared with degree (a local measure), betweenness and Eigenvector centralities (two global measures) in the case of biological network (Yeast interaction protein-protein network) and a linguistic network (Moby Thesaurus II). In both networks, the l-index has poor correlation with betweenness but correlates with degree and Eigenvector. Being a local measure, one can take advantage by using the l-index because it carries more information about its neighbors when compared with degree centrality, indeed it requires less time to compute when compared with Eigenvector centrality. Results suggests that l-index produces better results than degree and Eigenvector measures for ranking purposes, becoming suitable as a tool to perform this task.