Identification of Influential Nodes in Complex Networks With Degree and Average Neighbor Degree

Identifying influential nodes in complex networks is vital for understanding their structure and dynamic behavior. Although methods based on a single characteristic of nodes have been demonstrated to be effective in specific scenarios, the information they provide in dealing with the global aspect of the network is often limited or incomplete. In this paper, we present a measure that combines the degree and the average neighbor degree to evaluate the influence of nodes. As a supplement, we also propose the corresponding gravity index. Experiments on synthetic and real-world networks show that the method proposed in this paper is superior to previous methods based on the single characteristic of nodes in most scenarios. Our gravity strategy is also highly competitive compared with the current famous gravity method.