Hybrid Centrality Measures for Binary and Weighted Networks

TL;DR

This paper introduces hybrid centrality measures combining traditional metrics to better assess actor importance in social networks, especially weighted ones, demonstrating their effectiveness through co-authorship network analysis.

Contribution

The paper proposes novel hybrid centrality measures and their generalized forms for weighted networks, enhancing the understanding of actor importance beyond traditional measures.

Findings

Hybrid measures show stronger correlation with scholar performance.

Weighted hybrid measures outperform traditional centrality metrics.

Proposed measures effectively identify prominent actors in networks.

Abstract

Existing centrality measures for social network analysis suggest the im-portance of an actor and give consideration to actor's given structural position in a network. These existing measures suggest specific attribute of an actor (i.e., popularity, accessibility, and brokerage behavior). In this study, we propose new hybrid centrality measures (i.e., Degree-Degree, Degree-Closeness and Degree-Betweenness), by combining existing measures (i.e., degree, closeness and betweenness) with a proposition to better understand the importance of actors in a given network. Generalized set of measures are also proposed for weighted networks. Our analysis of co-authorship networks dataset suggests significant correlation of our proposed new centrality measures (especially weighted networks) than traditional centrality measures with performance of the scholars. Thus, they are useful measures which can be used instead of traditional measures to show prominence of the actors in a network.