LCH:A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks  

作　　者：Gui-Qiong Xu Lei Meng Deng-Qin Tu Ping-Le Yang 徐桂琼;孟蕾;涂登琴;杨平乐(Department of Information Management,School of Management,Shanghai University,Shanghai 200444,China;Business School,University of Shanghai for Science and Technology,Shanghai 200093,China;School of Electrical and Information Engineering,Jiangsu University of Science and Technology Zhangjiagang 215600,China)

机构地区：[1]Department of Information Management,School of Management,Shanghai University,Shanghai 200444,China [2]Business School,University of Shanghai for Science and Technology,Shanghai 200093,China [3]School of Electrical and Information Engineering,Jiangsu University of Science and Technology Zhangjiagang 215600,China

出　　处：《Chinese Physics B》2021年第8期566-574,共9页中国物理B（英文版）

基　　金：Project supported by the National Natural Foundation of China(Grant No.11871328);the Shanghai Science and Technology Development Funds Soft Science Research Project(Grant No.21692109800).

摘　　要：Identifying influential nodes in complex networks is one of the most significant and challenging issues,which may contribute to optimizing the network structure,controlling the process of epidemic spreading and accelerating information diffusion.The node importance ranking measures based on global information are not suitable for large-scale networks due to their high computational complexity.Moreover,they do not take into account the impact of network topology evolution over time,resulting in limitations in some applications.Based on local information of networks,a local clustering H-index(LCH)centrality measure is proposed,which considers neighborhood topology,the quantity and quality of neighbor nodes simultaneously.The proposed measure only needs the information of first-order and second-order neighbor nodes of networks,thus it has nearly linear time complexity and can be applicable to large-scale networks.In order to test the proposed measure,we adopt the susceptible-infected-recovered(SIR)and susceptible-infected(SI)models to simulate the spreading process.A series of experimental results on eight real-world networks illustrate that the proposed LCH can identify and rank influential nodes more accurately than several classical and state-of-the-art measures.

关 键 词：complex networks influential nodes local structure susceptible infected recovered model susceptible infected model 

分 类 号：O157.5[理学—数学]