Weighted information index mining of key nodes through the perspective of evidential distance

Abstract

Many strategies have been developed to mine critical nodes in the networks, however, most of them are based solely on the network topology, with no regard for the diversity of information between nodes. Using a weighted fusion method, we propose a method that considers neighboring nodes of second order and their similarity in this paper. Based on the evidence distance, an information distance Id is computed to find out how similar nodes are, and second-order adjacent nodes are used to reflect node information. Information distance is used to determine the weight of a node, and a weighted information index (WII) is used to evaluate how important a node is by combining the weight and information of nodes. The proposed method is tested by identifying influential nodes in twelve real-world networks and comparing their performance with the other seven methods. The Susceptible–Infected (SI) model and two evaluation indices have been applied to twelve real-world networks to evaluate the feasibility of our method. The findings indicate that the WII exhibits the most effective differentiation, thereby demonstrating the efficacy and practicality of our approach.