SLGC: Identifying influential nodes in complex networks from the perspectives of self-centrality, local centrality, and global centrality
Da Ai(艾达)1,†, Xin-Long Liu(刘鑫龙)1, Wen-Zhe Kang(康文哲)1, Lin-Na Li(李琳娜)1,2, Shao-Qing Lü(吕少卿)1, and Ying Liu(刘颖)1
1 School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China; 2 Network Ecological and Environmental Governance Research Center, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
Abstract Identifying influential nodes in complex networks and ranking their importance plays an important role in many fields such as public opinion analysis, marketing, epidemic prevention and control. To solve the issue of the existing node centrality measure only considering the specific statistical feature of a single dimension, a SLGC model is proposed that combines a node's self-influence, its local neighborhood influence, and global influence to identify influential nodes in the network. The exponential function of e is introduced to measure the node's self-influence; in the local neighborhood, the node's one-hop neighboring nodes and two-hop neighboring nodes are considered, while the information entropy is introduced to measure the node's local influence; the topological position of the node in the network and the shortest path between nodes are considered to measure the node's global influence. To demonstrate the effectiveness of the proposed model, extensive comparison experiments are conducted with eight existing node centrality measures on six real network data sets using node differentiation ability experiments, susceptible-infected-recovered (SIR) model and network efficiency as evaluation criteria. The experimental results show that the method can identify influential nodes in complex networks more accurately.
Received: 19 June 2023
Revised: 07 August 2023
Accepted manuscript online: 10 August 2023
PACS:
89.75.Fb
(Structures and organization in complex systems)
Fund: Project supported by the Natural Science Basic Research Program of Shaanxi Province of China (Grant No. 2022JQ- 675) and the Youth Innovation Team of Shaanxi Universities.
Corresponding Authors:
Da Ai
E-mail: aida@xupt.edu.cn
Cite this article:
Da Ai(艾达), Xin-Long Liu(刘鑫龙), Wen-Zhe Kang(康文哲), Lin-Na Li(李琳娜), Shao-Qing Lü(吕少卿), and Ying Liu(刘颖) SLGC: Identifying influential nodes in complex networks from the perspectives of self-centrality, local centrality, and global centrality 2023 Chin. Phys. B 32 118902
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