Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (12): 92-101.doi: 10.11896/jsjkx.241000090

• Database & Big Data & Data Science • Previous Articles     Next Articles

Identifying Influential Nodes in Multilayer Networks Based on Layer Weighting and Gravity Centrality

WANG Jianbo1,2,4, LUO Yu1, XU Xiaoke3, DU Zhanwei2, LI Ping1   

  1. 1 School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China
    2 School of Public Health, The University of Hong Kong, Hong Kong 999077, China
    3 Computational Communication Research Center and the School of Journalism and Communication, Beijing Normal University, Beijing 100875, China
    4 Key Laboratory of Intelligent Policing and National Security Risk Management, Sichuan Police College, Luzhou, Sichuan 646000, China
  • Received:2024-10-18 Revised:2025-02-15 Online:2025-12-15 Published:2025-12-09
  • About author:WANG Jianbo,born in 1980,Ph.D,lecturer,master’s supervisor.His main research interests include network science(complex networks),machine learning and deep learning algorithms and applications,and big data analysis.
  • Supported by:
    This work was supported by the Shenzhen-Hong Kong-Macau Science and Technology Project(Category C)(SGDX20230821091559022),National Natural Science Foundation of China(62173065),Natural Science Foundation of Beijing(4242040),Intelligent Policing and National Security Risk Management Laboratory Open Topics for the Year 2025(ZHKFYB2503) and Intelligent Policing and National Security Risk Ma-nagement Laboratory Open Topics for the Year 2024(ZHKFZD2401).

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Abstract: Identifying key nodes in multilayer networks is a major research focus in network science,as it plays a crucial role in understanding network structure and function.Inspired by the gravity model,most existing methods primarily rely on local or global topological information,often overlook the influence of intra-layer and inter-layer structures on nodes in multilayer networks.This oversight limits the effectiveness of node identification.To address this,this paper introduces a layer weighting and gravity centrality algorithm for identifying key nodes in multilayer networks.The algorithm first assigns weights to each network layer by considering both intra-layer and inter-layer structures,thus quantifying the influence of degree centrality across different layers.Next,it incorporates the impact of inter-layer structures on propagation paths to define the effective distance between nodes.Finally,the influence of each node within the entire network is calculated using a gravity-based formula.Extensive experiments on nine real-world networks show that the proposed algorithm offers higher accuracy and resolution compared to six benchmark methods.

Key words: Influential node, Gravity model, Centrality, Layer-weighted, Multilayer network

CLC Number: 

  • TP391
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