Ranking Influential Nodes in Complex Networks with Information Entropy Method

The ranking of influential nodes in networks is of great significance. Influential nodes play an enormous role during the evolution process of information dissemination, viral marketing, and public opinion control. The sorting method of multiple attributes is an effective way to identify the influential nodes. However, these methods offer a limited improvement in algorithm performance because diversity between different attributes is not properly considered. On the basis of the k-shell method, we propose an improved multiattribute k-shell method by using the iterative information in the decomposition process. Our work combines sigmod function and iteration information to obtain the position index. The position attribute is obtained by combining the shell value and the location index. The local information of the node is adopted to obtain the neighbor property. Finally, the position attribute and neighbor attribute are weighted by the method of information entropy weighting. The experimental simulations in six real networks combined with the SIR model and other evaluation measure fully verify the correctness and effectiveness of the proposed method.