Ranking influential spreaders based on both node k-shell and structural hole

排名(信息检索) 计算机科学 中心性 节点(物理) 壳体(结构) 单调函数 度量(数据仓库) 数据挖掘 算法 人工智能 数学 组合数学 结构工程 机械工程 工程类 数学分析
作者
Zhili Zhao,Ding Li,Yue Sun,Ruisheng Zhang,Jun Liu
出处
期刊:Knowledge Based Systems [Elsevier BV]
卷期号:260: 110163-110163 被引量:34
标识
DOI:10.1016/j.knosys.2022.110163
摘要

The ranking of individual spreaders aims to measure the influential capability of individual nodes and is important to control information spreading in a network. However, many ranking methods are either degree-based, k-shell-related or a combination of the two, which are not necessarily related to influential capability. Inspired by the strengths of the k-shell decomposition method, this work improves it on the basis of structural holes (SH) and proposes a novel ranking method, SHKS. Different from the efforts that aim only to improve the k-shell decomposition method, this work considers the k-shell and SH-based centrality of a node as well as its neighbors and second-order neighbors. Based on the flexible combination of k-shell and SH, SHKS can identify not only the core nodes with large k-shell indices but also the nodes that have small k-shell indices but play an important role in bridging different parts of a network. Experimental results show that SHKS presents better performance than baseline methods in terms of the Kendall τ correlation results, and the average improvements range from 1.3% to 121.1%. SHKS also has the best monotonicity, and its average monotonicity value on experimental networks is close to 0.99. Moreover, SHKS has good performance in identifying the most influential top-k nodes compared with baseline methods.
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