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

doi:10.1088/1674-1056/abea86

中国物理B ›› 2021, Vol. 30 ›› Issue (8): 88901-088901.doi: 10.1088/1674-1056/abea86

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

Gui-Qiong Xu(徐桂琼)^1,†, Lei Meng(孟蕾)¹, Deng-Qin Tu(涂登琴)^1,‡, and Ping-Le Yang(杨平乐)^2,3

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

收稿日期:2020-11-26 修回日期:2021-02-02 接受日期:2021-03-01 出版日期:2021-07-16 发布日期:2021-07-16
通讯作者: Gui-Qiong Xu, Deng-Qin Tu E-mail:xugq@staff.shu.edu.cn;shumse724@shu.edu.cn
基金资助:
Project supported by the National Natural Foundation of China (Grant No. 11871328) and the Shanghai Science and Technology Development Funds Soft Science Research Project (Grant No. 21692109800).

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

Gui-Qiong Xu(徐桂琼)^1,†, Lei Meng(孟蕾)¹, Deng-Qin Tu(涂登琴)^1,‡, and Ping-Le Yang(杨平乐)^2,3

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

Received:2020-11-26 Revised:2021-02-02 Accepted:2021-03-01 Online:2021-07-16 Published:2021-07-16
Contact: Gui-Qiong Xu, Deng-Qin Tu E-mail:xugq@staff.shu.edu.cn;shumse724@shu.edu.cn
Supported by:
Project supported by the National Natural Foundation of China (Grant No. 11871328) and the Shanghai Science and Technology Development Funds Soft Science Research Project (Grant No. 21692109800).

摘要/Abstract

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

Abstract: 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.

Key words: complex networks, influential nodes, local structure, susceptible infected recovered model, susceptible infected model

中图分类号: (Structures and organization in complex systems)

89.75.Fb

Gui-Qiong Xu(徐桂琼), Lei Meng(孟蕾), Deng-Qin Tu(涂登琴), and Ping-Le Yang(杨平乐). LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks[J]. 中国物理B, 2021, 30(8): 88901-088901.

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

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

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