Identifying influential nodes through an improved k-shell iteration factor model

Abstract

Measuring the importance of nodes is a rapidly developing field in network science, with centrality measures playing a critical role in various applications, including epidemic control, influence maximization, and network integration. The commonly used coreness calculated by the k-core decomposition process for identifying influential spreaders has limitations, which fail to consider the topological position of nodes and lead to low node discrimination. To address these issues, we propose a novel approach called the structural iteration factor ranking method to identify the influential nodes in complex networks, which considers the remaining structural properties when implementing the k-core decomposition. Our method considers both local and global hierarchy information and selects the neighbor information attribute to avoid problems with overlapping neighbor information propagation caused by the significant clustering coefficient of networks. We consider ground-truth influence ranking based on the Susceptible–Infected–Recovered model, on which the performance of the proposed ranking method is verified. Our proposed method outperforms previous methods in calculating monotonicity, similarity, and accuracy. It provides a more comprehensive evaluation of nodes and enables more in-depth identification of critical nodes. These findings imply that considering the remaining structural properties when implementing the k-core decomposition dominates the spreading strength of nodes and catches more details for ranking the node influence in complex networks.