Identifying vital nodes in complex network by considering multiplex influences

Identifying vital nodes is a fundamental topic in network science. Some methods are proposed to identify vital nodes in a complex network. These measures take into account different aspects of a node’s importance, such as its number of connections, the centrality of its connected nodes, and the distribution of its connections. Applying these measures makes it is possible to identify the nodes that play a vital role in the network and that have the greatest impact on its structure and function. However, there is still an inherent problem with identifying vital nodes accurately and discriminatively. To address the problem, for undirected unweighted networks, we propose an algorithm based on the nodes’ multiplex influences via the network structure to identify vital nodes. The effectiveness of the proposed method is evaluated by Kendall’s Tau ([Formula: see text]) and monotonicity and compared with well-known existing metrics such as degree centrality, K-shell decomposition, H-index, betweenness centrality, closeness centrality, eigenvector centrality, collective influence, and gravity model in 10 real networks. Experimental results show the superiority of the proposed algorithm in identifying vital nodes.