A Key Node Mining Method Based on K-Shell and Neighborhood Information

Abstract

Mining key nodes in complex networks has always been a promising research direction in the field of complex networks. Many precise methods proposed by researchers for mining influential special nodes in networks have been widely applied in a plethora of fields. However, some important node-mining methods often use the degree as a node attribute indicator for evaluating node importance, while the clustering coefficient, as an important attribute of nodes, is rarely utilized. Some methods only consider the global position of nodes in the network while ignoring the local structural information of nodes in special positions and the network. Hence, this paper introduces a novel node centrality method, KCH. The KCH method leverages K-shell to identify the global position of nodes and assists in evaluating the importance of nodes by combining information such as structural holes and local clustering coefficients of first-order neighborhoods. This integrated approach yields an enhanced performance compared to existing methods. We conducted experiments on connectivity, monotonicity, and zero models on 10 networks to evaluate the performance of KCH. The experiments revealed that when compared to the collective influence baseline methods, such as social capital and hierarchical K-shell, the KCH method exhibited superior capabilities in terms of collective influence.