A novel method for identifying influential nodes in complex networks based on multiple attributes

Identifying influential nodes is a crucial issue in epidemic spreading, controlling the propagation process of information and viral marketing. Thus, algorithms for exploring vital nodes have aroused more and more concern among researchers. Recently, scholars have proposed various types of algorithms based on different perspectives. However, each of these methods has their own strengths and weaknesses. In this work, we introduce a novel multiple attributes centrality for identifying significant nodes based on the node location and neighbor information attributes. We call our proposed method the MAC. Specifically, we utilize the information of the number of iterations per node to enhance the accuracy of the K-shell algorithm, so that the location attribute can be used to distinguish the important nodes more deeply. And the neighbor information attribute we selected can effectively avoid the overlapping problem of neighbor information propagation caused by large clustering coefficient of networks. Because these two indexes have different emphases, we use entropy method to assign them reasonable weights. In addition, MAC has low time complexity O(n), which makes the algorithm suitable for large-scale networks. In order to objectively assess its performance, we utilize the Susceptible-Infected-Recovered (SIR) model to verify the propagation capability of each node and compare the MAC method with several classic methods in six real-life datasets. Extensive experiments verify the superiority of our algorithm to other comparison algorithms.