Ranking nodes in complex networks based on TsRank

It is theoretically and practically meaningful to rank and identify nodes in complex networks in various fields, however, many existing methods consider single feature of graph. To utilize multiple attributes of graph, a novel ranking method based on Tsallis entropy is proposed in this paper, which considers information transfer efficiency as global information of nodes and takes extended mixed degree and core neighborhood centrality as local information of nodes. We utilize the monotonicity function index, cumulative distribution (CDF), Kendall’s tau coefficient, Jaccard similarity coefficient, and the total number of infected nodes based on susceptible-infected-recovered (SIR) model as evaluation metrics to measure the performance of the proposed method. The simulation results demonstrate that the proposed method has great superiority in terms of monotonicity, resolution, the accuracy of both the whole ranking results and top-c ranked nodes, and spreading ability of the top-10 nodes.