Research on Node Importance Identification for Temporal Network Based on D-S Evidence Theory
The eigenvector centrality is one of the effective methods to measure the importance of complex network nodes,but the eigenvector centrality cannot measure the global importance of the nodes in temporal network.Therefore,D-S evidence theory is introduced.The importance of nodes in each time layer is taken as an information source,and multi-source information fusion is car-ried out by Dempster's rule to obtain the global importance of nodes.Experimental results on Enron and Workspace datasets show that the centrality of temporal network based on D-S evidence theory is 22.9%and 21.8%higher than the Spearman correlation coef-ficient of other methods.It is shown that the method can effectively apply the eigenvector centrality to measure the global importance of the nodes.
evidence theoryeigenvector centralityimportance of nodestemporal network
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