Abstract: This paper proposes a new synthetic measure of node centrality, namely, multi-information fused degree of grey incidence centrality (MIFDC), which is used to evaluate the importance of nodes and identify influential nodes in complex networks. It is the first time that the grey incidence analysis (GIA) and the D-S evidence theory are combined to identify influential nodes in complex networks in the MIFDC method. The proposed MIFDC measure comprehensively considers the information of multiple centrality measures and can correct the subjective bias problem in the selection process of the grey incidence operator. To verify the performance of the proposed method, the MIFDC method is applied to identify influential nodes in two real networks, the Advanced Research Project Agency (ARPA) network, and the terrorist relationship network. The application results show that the MIFDC method can effectively identify the influential nodes of the network.  

Key words: Complex Networks, Influential Nodes, Degree of Grey Incidence, D-S Evidence Theory, Information Fusion