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复杂系统与复杂性科学  2023, Vol. 20 Issue (4): 33-39    DOI: 10.13306/j.1672-3813.2023.04.005
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基于三元闭包模体的关键节点识别方法
徐越, 刘雪明
华中科技大学人工智能与自动化学院,武汉 430074
Method for Identifying Critical Nodes Based on Closed Triangle Motifs
XU Yue, LIU Xueming
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
全文: PDF(2128 KB)  
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摘要 复杂网络中的关键节点能够影响系统功能,许多真实网络中存在数目显著的三元闭包模体,为了探索该模体对节点重要度的影响,提出了基于三元闭包模体的关键节点识别方法。该算法衡量了各个模体的重要度,通过模体权重和节点度来评估节点重要度。在6个真实网络中,进行了鲁棒性实验和基于SIR模型的传播实验。实验结果表明,相比于度中心性DC、K-shell分解、WL中心性、映射熵ME方法,该算法能够更加有效地识别出网络中的关键节点。
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徐越
刘雪明
徐越
刘雪明
关键词 复杂网络关键节点三元闭包模体鲁棒性    
Abstract:Critical nodes in complex networks can influence the system functionality. Many real networks have a significant number of closed triangle motifs. To explore the influence of these motifs on the importance of nodes, a critical nodes identification method based on closed triangle motifs is proposed. The algorithm measures the importance of each motif and evaluates the node importance through the motif weights and node degrees. Robustness experiments and propagation experiments based on the SIR model are carried out with six real networks. The experimental results show that this method can identify critical nodes of the network more effectively than the DC method, K-shell method, WL method, and ME method.
Key wordscomplex networks    critical nodes    closed triangle motifs    robustness
收稿日期: 2022-01-22      出版日期: 2023-12-28
:  N949  
基金资助:国家自然科学基金(62172170)
通讯作者: 刘雪明(1988-),女,湖南娄底人,博士,副教授,主要研究方向为复杂网络。   
作者简介: 徐越(1997-),女,浙江衢州人,硕士,主要研究方向为复杂网络。
引用本文:   
徐越, 刘雪明. 基于三元闭包模体的关键节点识别方法[J]. 复杂系统与复杂性科学, 2023, 20(4): 33-39.
XU Yue, LIU Xueming. Method for Identifying Critical Nodes Based on Closed Triangle Motifs[J]. Complex Systems and Complexity Science, 2023, 20(4): 33-39.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2023.04.005      或      https://fzkx.qdu.edu.cn/CN/Y2023/V20/I4/33
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