Betweenness centrality of teams in social networks

TL;DR

This paper introduces a method to measure team influence in social networks using betweenness centrality on hypergraphs, revealing that weighted hyperedges restore power-law distribution and highlight influential teams.

Contribution

It proposes a novel approach to quantify team influence in hypergraphs, accounting for hyperedge weights, and analyzes the distribution behavior in scale-free hypergraphs.

Findings

Team influence correlates with widely connected members.

Weighted hyperedges restore power-law behavior in BC distribution.

Team influence can be effectively measured in hypergraph models.

Abstract

Betweenness centrality (BC) was proposed as an indicator of the extent of an individual's influence in a social network. It is measured by counting how many times a vertex (i.e., an individual) appears on all the shortest paths between pairs of vertices. A question naturally arises as to how the influence of a team or group in a social network can be measured. Here, we propose a method of measuring this influence on a bipartite graph comprising vertices (individuals) and hyperedges (teams). When the hyperedge size varies, the number of shortest paths between two vertices in a hypergraph can be larger than that in a binary graph. Thus, the power-law behavior of the team BC distribution breaks down in scale-free hypergraphs. However, when the weight of each hyperedge, for example, the performance per team member, is counted, the team BC distribution is found to exhibit power-law behavior. We find that a team with a widely connected member is highly influential.