A novel ranking approach for identifying crucial spreaders in complex networks based on Tanimoto Correlation

Abstract

The identification and rank of crucial spreaders aim to survey the diffusion capability, which has a significant impact on controlling information spread in networks. However, most of the findings neglect the fused information of the structural hole and neighbors within two orders. In addition, they fail to focus on the effect brought by the association strength between a pair of different neighborhood sets. To solve these issues, in this work, we design a novel semi-local ranking algorithm for achieving better recognition effects, referred to as the Extended Tanimoto Correlation (ETC). We first construct a topological structure of neighborhood sets Cluster, and define HierarchyVectorofCluster based on a novel layered approach IKsD. Considering the structural hole, Tanimoto Correlation (TC) is presented to contribute to measuring the association strength between different clusters. Additionally, we explicitly extend the Tanimoto Correlation metric on semi-local information to calculate each spreader’s influence score. Simulation experiments employ capability indicators and correctness metrics to apply in various networks. Results demonstrate that our method outperforms state-of-the-art algorithms in identifying and ranking effects for good spreading influence.