Bridging Centrality: Identifying Bridging Nodes In Scale-free Networks

Several centrality measures were introduced to identify essential components and compute components’ importance in networks. Majority of these centrality measures are dominated by components’ degree due to their nature of looking at networks’ topology. We propose a novel essential component identification model, bridging centrality, based on information flow and topological locality in scale-free networks. Bridging centrality provides an entirely new way of scrutinizing network structures and measuring components’ importance. We apply bridging centrality on real world networks, including one simulated network, two biological networks, two social networks, and one web network, and show that the nodes distinguished by bridging centrality are well located on the connecting positions between highly connected regions through analyzing the clustering coefficient and average path length of those networks. Bridging centrality can discriminate bridging nodes, the nodes with more information flowed through them and locations between highly connected regions, while other centrality measures can not.