Ranking Hubs in Weighted Networks with Node Centrality and Statistics

Hubs is a type of important nodes in complex networks and always play an influential or prominent roles in real networks. Node centrality of networks is an important measure and usually was used to detect hubs. Although many approaches to calculate node centrality are available, but node centrality of weighted complex networks need further investigation. In this paper, we develop a novel algorithm that works well for identifying hubs in weighted networks with node centrality. Our algorithm calculates a scores of node centrality of each candidate hub which was estimated with a statistic. We demonstrate that detected hubs by using this statistic is more reliable and interpretable than only with weighted node centrality.