Please wait a minute...
文章检索
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
  本期目录 | 过刊浏览 | 高级检索 |
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)  
输出: BibTeX | EndNote (RIS)      
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。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
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
ZTFLH:  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. 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
[1] LI X Y, LI W K, ZENG M, et al. Network-based methods for predicting essential genes or proteins: a survey[J]. Briefings in Bioinformatics, 2020, 21(2): 566-583.
[2] ZHANG Y P, BAO Y Y, ZHAO S, et al. Identifying node importance by combining betweenness centrality and katz centrality [C]// Proc of International Conference on CCBD. Piscataway, NJ: IEEE Press, 2015: 354-357.
[3] ALBERT R, JEONG H, BARABASI A L. Internet: diameter of the world-wide web[J]. Nature, 1999, 401(6): 130-131.
[4] FREEMAN L C. A set of measures of centrality based on betweenness[J]. Sociometry, 1977, 40(1): 35-41.
[5] KITSAK M, GALLOS L K, HAVLIN S, et al. Identification of influential spreaders in complex networks[J]. Nature Physics, 2010, 6(11): 888-893.
[6] 王建伟, 荣莉莉, 郭天柱. 一种基于局部特征的网络节点重要性度量方法[J]. 大连理工大学学报, 2010, 50(5): 822-826.
WANG J W, RONG L L, GUO T Z. A network node importance measurement method based on local characteristics[J]. Journal of Dalian University of Technology, 2010, 50(5): 822-826.
[7] NIE T Y, GUO Z, ZHAO K, et al. Using mapping entropy to identify node centrality in complex networks[J]. Physica A: Statistical Mechanics and Its Applications, 2016, 453: 290-297.
[8] MILO R, SHEN O S, LTZKOVITZ S, et al. Network motifs: simple building blocks of complex networks[J]. Science, 2002, 298(5594): 824-827.
[9] BENSON A R, GLEICH D F, LESKOVEC J. Higher-order organization of complex networks[J]. Science, 2016, 353(6295): 163-166.
[10] SHEN O S, MILO R, MANGAN S, et al. Network motifs in the transcriptional regulation network of Escherichia coli[J]. Nature Genetics, 2002, 31(1): 64-68.
[11] SHIZUKA D, MCDONALD D B. The network motif architecture of dominance hierarchies[J]. Journal of the Royal Society Interface, 2015, 12(105): 20150080.
[12] WANG P, LYU J H, YU X H. Identification of important nodes in directed biological networks: a network motif approach[J]. Plos One, 2014, 9(8): e106132.
[13] TOPIRCEANU A, DUMA A, UDRESCU M. Uncovering the fingerprint of online social networks using a network motif based approach[J]. Computer Communications, 2016, 73(1): 167-175.
[14] BIANCONI G, DARST R K, IACOVACCI J, et al. Triadic closure as a basic generating mechanism of communities in complex networks[J]. Physical Review E: Statistical Nonlinear and Soft Matter Physics, 2014, 90(4): 042806.
[15] LI P Z, HUANG L, WANG C D, et al. Community detection using attribute homogenous motif[J]. IEEE Access, 2018, 6: 47707-47716.
[16] XU M Q, PAN Q, MUSCOLONI A, et al. Modular gateway-ness connectivity and structural core organization in maritime network science[J]. Nature Communications, 2020, 11(1): 1-15.
[17] KOVACS I A, BARABASI A L. Destruction perfected[J]. Nature, 2015, 524(7563): 38-9.
[18] SIEW C S Q. The orthographic similarity structure of English words: insights from network science[J]. Applied Network Science, 2018, 3(1): 1-18.
[19] LIU Z H, JIANG C, WANG J Y, et al. The node importance in actual complex networks based on a multi-attribute ranking method[J]. Knowledge-Based Systems, 2015, 84(8): 56-66.
[20] ATKESON A. On using SIR models to model disease scenarios for COVID-19[J]. Quarterly Review, 2020, 41(1): 1-35.
[21] MAJI G, MANDAL S, SEN S. A systematic survey on influential spreaders identification in complex networks with a focus on K-shell based techniques[J]. Expert Systems with Applications, 2020, 16: 113681.
[22] CHEN C Y, ZHAO Y, GAO J X, et al. Nonlinear model of cascade failure in weighted complex networks considering overloaded edges[J]. Scientific Reports, 2020, 10(1): 1-12.
[23] HUANG K K, WANG Z, JUSUP M. Incorporating latent constraints to enhance inference of network structure[J]. IEEE Transactions on Network Science and Engineering, 2018, 7(1): 466-475.
[24] DEY A K, GEL Y R, POOR H V. What network motifs tell us about resilience and reliability of complex networks[J]. Proceedings of the National Academy of Sciences, 2019, 116(39): 19368-19373.
[25] SHAO C C, CIAMPAGLIA G L, VAROL O, et al. The spread of low-credibility content by social bots[J]. Nature Communications, 2018, 9(1): 1-9.
[26] LI X, LIU Y Y, ZHAO C L, et al. Locating multiple sources of contagion in complex networks under the SIR model[J]. Applied Sciences, 2019, 9(20): 4472.
[27] CASTELLANO C, PASTOR S R. Thresholds for epidemic spreading in networks[J]. Physical Review Letters, 2010, 105(21): 218701.
[28] CHEN D B, LYU L Y, SHANG M S, et al. Identifying influential nodes in complex networks[J]. Physica A: Statistical Mechanics and Its Applications, 2012, 391(4): 1777-1787.
[29] POSSI R, AHMED N. The network data repository with interactive graph analytics and visualization [C]// Proc of 29th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2015: 4292-4293.
[1] 董昂, 吴亚丽, 任远光, 冯梦琦. 基于局部熵的级联故障模型初始负载定义方式[J]. 复杂系统与复杂性科学, 2023, 20(4): 18-25.
[2] 董志良, 贾妍婧, 安海岗. 产业部门间间接能源流动及依赖关系演化特征[J]. 复杂系统与复杂性科学, 2023, 20(4): 61-68.
[3] 马亮, 金福才, 胡宸瀚. 中国铁路快捷货物运输网络复杂性分析[J]. 复杂系统与复杂性科学, 2023, 20(4): 26-32.
[4] 杨文东, 黄依宁, 张生润. 基于多层复杂网络的RCEP国际航线网络特征分析[J]. 复杂系统与复杂性科学, 2023, 20(3): 60-67.
[5] 任翠萍, 杨明翔, 张裕铭, 谢逢洁. 快递安全事故致因网络构建及结构特性分析[J]. 复杂系统与复杂性科学, 2023, 20(2): 74-80.
[6] 曾茜, 韩华, 李秋晖, 李巧丽. 基于分包的混合朴素贝叶斯链路预测模型[J]. 复杂系统与复杂性科学, 2023, 20(2): 10-19.
[7] 林兆丰, 李树彬, 孔祥科. 地铁建设对公交系统鲁棒性的影响[J]. 复杂系统与复杂性科学, 2023, 20(1): 66-73.
[8] 李巧丽, 韩华, 李秋晖, 曾茜. 基于最优路径相似度传输矩阵的链路预测方法[J]. 复杂系统与复杂性科学, 2023, 20(1): 9-17.
[9] 路冠平, 李江平. 基于复杂网络演化模型的新冠危机对经济的冲击研究[J]. 复杂系统与复杂性科学, 2023, 20(1): 34-40.
[10] 王淑良, 陈辰, 张建华, 栾声扬. 基于复杂网络的关联公共交通系统韧性分析[J]. 复杂系统与复杂性科学, 2022, 19(4): 47-54.
[11] 郭明健, 高岩. 基于复杂网络理论的电力网络抗毁性分析[J]. 复杂系统与复杂性科学, 2022, 19(4): 1-6.
[12] 卢炯, 许新建. 协同对社会传播的影响[J]. 复杂系统与复杂性科学, 2022, 19(3): 14-19.
[13] 张健, 宋志刚, 张雨. 基于节点重要性的建筑群火灾蔓延高危建筑的确定方法[J]. 复杂系统与复杂性科学, 2022, 19(3): 66-73.
[14] 肖琴, 罗帆. 基于复杂网络的通用航空安全监管演化博弈研究[J]. 复杂系统与复杂性科学, 2022, 19(3): 33-43.
[15] 肖瑶, 李守伟, 王怡涵. FPGA芯片产业链及知识转移网络特征分析[J]. 复杂系统与复杂性科学, 2022, 19(3): 20-26.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed