Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们 横山亮次奖 百年刊庆
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  横山亮次奖  |  百年刊庆
清华大学学报(自然科学版)  2022, Vol. 62 Issue (5): 849-861    DOI: 10.16511/j.cnki.qhdxxb.2022.25.041
  专题:漏洞分析与风险评估 本期目录 | 过刊浏览 | 高级检索 |
基于K-shell的复杂网络关键节点识别方法
谢丽霞1, 孙红红1, 杨宏宇1,2, 张良3
1. 中国民航大学 计算机科学与技术学院, 天津 300300, 中国;
2. 中国民航大学 安全科学与工程学院, 天津 300300, 中国;
3. 亚利桑那大学 信息学院, 图森 85721, 美国
Key node recognition in complex networks based on the K-shell method
XIE Lixia1, SUN Honghong1, YANG Hongyu1,2, ZHANG Liang3
1. College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China;
2. College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China;
3. College of Information, University of Arizona, Tucson 85721, USA
全文: PDF(2608 KB)   HTML
输出: BibTeX | EndNote (RIS)      
摘要 针对复杂网络中关键节点识别方法的分辨率和准确性不足的问题,该文提出了一种基于K-shell的复杂网络关键节点识别方法(K-shell based key node recognition method,KBKNR)。首先,采用K-shell方法将网络分层,获取每个节点的K壳(K-shell,Ks)值,通过Ks值衡量复杂网络全局结构的影响。其次,提出综合度(comprehensive degree,CD)的概念,并设定可动态调整的影响系数μi,通过平衡邻居节点和次邻居节点的不同影响程度,获取每个节点的综合度。在该方法中,当节点Ks值相同时,综合度较大的节点更重要。对比几种经典关键节点识别方法和一种风险评估方法,实验结果表明,该方法能够有效识别关键节点,在不同复杂网络中具有较高的准确率和分辨率。除此之外,KBKNR方法可以为网络节点的风险评估、重要节点保护和网络中节点的风险处置优先级排序提供依据。
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
谢丽霞
孙红红
杨宏宇
张良
关键词 复杂网络K-shell综合度邻居节点节点重要性    
Abstract:Key node recognition methods for complex networks often have insufficient resolution and accuracy. This study developed a K-shell based key node recognition method for complex networks that first stratifies the network to obtain the K-shell (Ks) values for each node that indicate the influence of the global structure of the complex network. A comprehensive degree (CD) was then defined that balances the various influences of neighboring nodes and sub-neighboring nodes. A dynamic adjustable influence coefficient, μi, was also defined. Nodes with the same Ks but larger comprehensive degrees are more important. Tests show that this method more effectively identifies key nodes than several classical key node recognition methods and a risk assessment method, and has high accuracy and resolution in different complex networks. This method provides network node risk assessments that can be used to protect important nodes and to determine the risk disposal priority of the network nodes.
Key wordscomplex networks    K-shell    comprehensive degree    neighboring nodes    node importance
收稿日期: 2021-09-15      出版日期: 2022-04-26
基金资助:国家自然科学基金民航联合研究项目(U1833107)
通讯作者: 杨宏宇,教授,E-mail:yhyxlx@hotmail.com      E-mail: yhyxlx@hotmail.com
作者简介: 谢丽霞(1974—),女,教授。
引用本文:   
谢丽霞, 孙红红, 杨宏宇, 张良. 基于K-shell的复杂网络关键节点识别方法[J]. 清华大学学报(自然科学版), 2022, 62(5): 849-861.
XIE Lixia, SUN Honghong, YANG Hongyu, ZHANG Liang. Key node recognition in complex networks based on the K-shell method. Journal of Tsinghua University(Science and Technology), 2022, 62(5): 849-861.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2022.25.041  或          http://jst.tsinghuajournals.com/CN/Y2022/V62/I5/849
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
[1] YU E Y, FU Y, TANG Q, et al. A re-ranking algorithm for identifying influential nodes in complex networks[J]. IEEE Access, 2020, 8:211281-211290.
[2] YAN X L, CUI Y P, NI S J. Identifying influential spreaders in complex networks based on entropy weight method and gravity law[J]. Chinese Physics B, 2020, 29(4):048902.
[3] ULLAH A, WANG B, SHENG J F, et al. Identification of influential nodes via effective distance-based centrality mechanism in complex networks[J]. Complexity, 2021, 2021:8403738.
[4] QIU L Q, ZHANG J Y, TIAN X B. Ranking influential nodes in complex networks based on local and global structures[J]. Applied Intelligence, 2021, 51(7):4394-4407.
[5] LIU J G, REN Z M, GUO Q. Ranking the spreading influence in complex networks[J]. Physica A:Statistical Mechanics and its Applications, 2013, 392(18):4154-4159.
[6] ZENG A, ZHANG C J. Ranking spreaders by decomposing complex networks[J]. Physics Letters A, 2013, 377(14):1031-1035.
[7] NAMTIRTHA A, DUTTA A, DUTTA B. Weighted K-shell degree neighborhood:A new method for identifying the influential spreaders from a variety of complex network connectivity structures[J]. Expert Systems with Applications, 2020, 139:112859.
[8] BERAHMAND K, BOUYER A, SAMADI N. A new local and multidimensional ranking measure to detect spreaders in social networks[J]. Computing, 2019, 101(11):1711-1733.
[9] BAE J, KIM S. Identifying and ranking influential spreaders in complex networks by neighborhood coreness[J]. Physica A:Statistical Mechanics and its Applications, 2014, 395:549-559.
[10] KITSAK M, GALLOS L K, HAVLIN S, et al. Identification of influential spreaders in complex networks[J]. Nature Physics, 2010, 6(11):888-893.
[11] IBNOULOUAFI A, EL HAZITI M, CHERIFI H. M-centrality:Identifying key nodes based on global position and local degree variation[J]. Journal of Statistical Mechanics:Theory and Experiment, 2018, 2018(7):073407.
[12] MAJI G. Influential spreaders identification in complex networks with potential edge weight based K-shell degree neighborhood method[J]. Journal of Computational Science, 2020, 39:101055.
[13] 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, 161:113681.
[14] ZHANG D Y, WANG Y, ZHANG Z X. Identifying and quantifying potential super-spreaders in social networks[J]. Scientific Reports, 2019, 9(1):14811.
[15] 中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. 信息安全技术信息安全风险评估规范:GB/T 20984-2007[S]. 北京:中国标准出版社, 2007. General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the People's Republic of China. Information security technology-Risk assessment specification for information security:GB/T 20984-2007[S]. Beijing:Standards Press of China, 2007. (in Chinese)
[16] 彭俊好, 徐国爱, 杨义先, 等. 基于效用的安全风险度量模型[J]. 北京邮电大学学报, 2006, 29(2):59-61, 69. PENG J H, XU G A, YANG Y X, et al. Measure model of security risk based on utility[J]. Journal of Beijing University of Posts and Telecommunications, 2006, 29(2):59-61, 69. (in Chinese)
[1] 李自圆, 孙昊, 李林波. 基于手机信令数据的长三角全域城际出行网络分析[J]. 清华大学学报(自然科学版), 2022, 62(7): 1203-1211.
[2] 郁湧, 王莹港, 罗正国, 杨燕, 王鑫锴, 高涛, 于倩. 基于聚类系数和节点中心性的链路预测算法[J]. 清华大学学报(自然科学版), 2022, 62(1): 98-104.
[3] 丁莹, 张健钦, 杨木, 宫鹏, 贾礼朋, 邓少存. 新冠疫情发生城市仿真模型及防控措施评价——以武汉市为例[J]. 清华大学学报(自然科学版), 2021, 61(12): 1452-1461.
[4] 倪顺江, 翁文国, 张辉. 大规模传染病传播围堵策略的模拟研究[J]. 清华大学学报(自然科学版), 2016, 56(1): 97-101.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《清华大学学报(自然科学版)》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn