Betweenness centrality of teams in social networks

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.

References in corpus (26)

• Emergence of scaling in random networks
• Statistical mechanics of complex networks
• The structure and function of complex networks
• Community structure in social and biological networks
• Assortative mixing in networks
• The structure of scientific collaboration networks
• Evolution of networks
• Universal Behavior of Load Distribution in Scale-free Networks
• Evolutionary dynamics of higher-order interactions in social networks
• Classification of scale-free networks
• Random hypergraphs and their applications
• Traffic on complex networks: Towards understanding global statistical properties from microscopic density fluctuations
• Social contagion models on hypergraphs
• Random walks on hypergraphs
• From the betweenness centrality in street networks to structural invariants in random planar graphs
• Robust classification of salient links in complex networks
• Simplicial SIS model in scale-free uniform hypergraph
• Information Super-Diffusion on Structured Networks
• Hypergraph topological quantities for tagged social networks
• Recent advances of percolation theory in complex networks
• Academic team formation as evolving hypergraphs
• Load distribution in weighted complex networks
• Homological percolation transitions in growing simplicial complexes
• Population stratification using a statistical model on hypergraphs
• Thermodynamics of spin systems on small-world hypergraphs
• Small-world hypergraphs on a bond-disordered Bethe lattice

Cited by in corpus (3)

• Scientific success from the perspective of the strength of weak ties
• $(k,q)$-core decomposition of hypergraphs
• Topological estimation of the latent geometry of a complex network