Identifying influential nodes in complex networks using a gravity model based on the H-index method
Siqi Zhu, Jie Zhan, Xing Li
Abstract
Identifying influential spreaders in complex networks is a widely discussed topic in the field of network science. Numerous methods have been proposed to rank key nodes in the network, and while gravity-based models often perform well, most existing gravity-based methods either rely on node degree, k-shell values, or a combination of both to differentiate node importance without considering the overall impact of neighboring nodes. Relying solely on a node's individual characteristics to identify influential spreaders has proven to be insufficient. To address this issue, we propose a new gravity centrality method called HVGC, based on the H-index. Our approach considers the impact of neighboring nodes, path information between nodes, and the positional information of nodes within the network. Additionally, it is better able to identify nodes with smaller k-shell values that act as bridges between different parts of the network, making it a more reasonable measure compared to previous gravity centrality methods. We conducted several experiments on 10 real networks and observed that our method outperformed previously proposed methods in evaluating the importance of nodes in complex networks.