Betweenness centrality of teams in social networks

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