[1]严家萌,许立波,李兴森,等.可拓聚类的科教人际网络节点重要性动态分析方法[J].智能系统学报,2019,14(05):915-921.[doi:10.11992/tis.201811012]
 YAN Jiameng,XU Libo,LI Xingsen,et al.Dynamic analysis method of importance of science and education interpersonal network nodes based on extension clustering[J].CAAI Transactions on Intelligent Systems,2019,14(05):915-921.[doi:10.11992/tis.201811012]
点击复制

可拓聚类的科教人际网络节点重要性动态分析方法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年05期
页码:
915-921
栏目:
出版日期:
2019-09-05

文章信息/Info

Title:
Dynamic analysis method of importance of science and education interpersonal network nodes based on extension clustering
作者:
严家萌12 许立波2 李兴森3 庞超逸2 董瑞辰4
1. 浙江大学 工程师学院, 浙江 杭州 310015;
2. 浙江大学 宁波理工学院 计算机与数据工程学院, 浙江 宁波 315100;
3. 广东工业大学 可拓学与创新方法研究所, 广东 广州 510006;
4. 北京邮电大学 国际学院, 北京 100876
Author(s):
YAN Jiameng12 XU Libo2 LI Xingsen3 PANG Chaoyi2 DONG Ruichen4
1. Polytechnic Institute, Zhejiang University, Hangzhou 310015, China;
2. School of Computer and Data Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China;
3. Research Institute of Extenics and Innovation Methods, Guangdong University of Technology, Guangzhou 510006, China;
4. Department of International School, Beijing University of Posts and Telecommunications, Beijing 100876, China
关键词:
复杂网络节点重要性多属性可拓学可拓聚类可拓理论物元关联函数
Keywords:
complex networknode importancemulti-attributeextenicsextension clusteringextension theorymatter elementcorrelation function
分类号:
TP301.6
DOI:
10.11992/tis.201811012
摘要:
目前大多数研究对复杂社会网络关键节点影响力的识别都是静态的,缺乏动态变化的分析。采用可拓聚类方法对动态变化下的科教人际网络进行量化分析,首先以多属性决策法计算每个节点重要性,再利用变异系数权重法计算得该节点综合重要性量值,之后划分等级并取标准正域和正域区间,利用可拓关联函数计算每个节点与每个等级的关联度,关联度值最大的等级即为该节点对应等级,最后分析同一社会网络节点在不同时间点的重要性等级变化。可拓聚类方法尝试从动态上对网络节点重要性进行把握,最后通过实例验证了该方法的有效性。
Abstract:
At present, the identification of the influence of key nodes in complex social network is usually static and does not involve the analysis of dynamic changes. The extension clustering method is used to quantitatively analyze the interpersonal network of science and education under dynamic changes. First, the importance of each node is calculated by multi-attribute decision-making method. Then the comprehensive importance of the node is calculated by the coefficient of variation weight method. It is then classified, and the standard positive domain and the positive domain are acquired. The extension correlation function is used to calculate the degree of association between each node and each level. The level with the highest correlation value is the corresponding level of the node. Finally, the importance level of the same social network node at different time points is analyzed. The extension clustering method aims to dynamically determine the importance of network nodes. Finally, the effectiveness of the method is verified using an example.

参考文献/References:

[1] ADAMIC L A, HUBERMAN B A, BARABÁSI A L, et al. Power-law distribution of the world wide web[J]. Science, 2000, 287(5461):2115.
[2] 于会, 刘尊, 李勇军. 基于多属性决策的复杂网络节点重要性综合评价方法[J]. 物理学报, 2013, 62(2):020204 YU Hui, LIU Zun, LI Yongjun. Key nodes in complex networks identified by multi-attribute decision-making method[J]. Acta physica sinica, 2013, 62(2):020204
[3] ALBERT R, JEONG H, BARABÁSI A L. Error and attack tolerance of complex networks[J]. Nature, 2000, 406(6794):378-382.
[4] FREEMAN L C. A set of measures of centrality based on betweenness[J]. Sociometry, 1977, 40(1):35-41.
[5] WEHMUTH K, ZIVIANI A. DACCER:distributed assessment of the closeness centrality ranking in complex networks[J]. Computer networks, 2013, 57(13):2536-2548.
[6] KITSAK M, GALLOS L K, HAVLIN S, et al. Identification of influential spreaders in complex networks[J]. Nature physics, 2010, 6(11):888-893.
[7] PASTOR-SATORRAS R, VESPIGNANI A. Epidemic spreading in scale-free networks[J]. Physical review letters, 2001, 86(14):3200-3203.
[8] FREEMAN L C. Centrality in social networks conceptual clarification[J]. Social networks, 1979, 1(3):215-239.
[9] OPSAHL T, AGNEESSENS F, SKVORETZ J. Node centrality in weighted networks:generalizing degree and shortest paths[J]. Social networks, 2010, 32(3):245-251.
[10] SABIDUSSI G. The centrality index of a graph[J]. Psychometrika, 1966, 31(4):581-603.
[11] CLAUSET A, SHALIZI C R, NEWMAN M E J. Power-law distributions in empirical data[J]. SIAM review, 2009, 51(4):661-703.
[12] CARMI S, HAVLIN S, KIRKPATRICK S, et al. A model of Internet topology using k-shell decomposition[J]. Proceedings of the national academy of sciences of the United States of America, 2007, 104(27):11150-11154.
[13] 杨春燕, 蔡文. 可拓学[M]. 北京:科学出版社, 2014.
[14] 杨国为, 王守觉. 模式可拓识别及其神经网络模型[J]. 哈尔滨工业大学学报, 2006, 38(7):1129-1132 YANG Guowei, WANG Shoujue. Pattern extension recognition and its neural network model[J]. Journal of Harbin Institute of Technology, 2006, 38(7):1129-1132
[15] XU Libo, LI Xingsen, PANG Chaoyi. Uncertain multiattribute decision-making based on interval number with extension-dependent degree and regret aversion[J]. Mathematical problems in engineering, 2018, 2018:6508636.
[16] XU Libo, LI Xingsen, SHAO Junkai, et al. Extension dependent degree method with mapping transformation for three-parameter interval number decision making[J]. Mathematical problems in engineering, 2018, 2018:1831086.
[17] 王晓磊, 杨岳湘, 何杰. 基于多重属性的P2P网络节点重要性度量方法[J]. 计算机应用, 2014, 34(S2):7-10, 19 WANG Xiaolei, YANG Yuexiang, HE Jie. P2P network node importance measurement method based on multi-attribute[J]. Journal of computer applications, 2014, 34(S2):7-10, 19
[18] 胡庆成, 尹龑燊, 马鹏斐, 等. 一种新的网络传播中最有影响力的节点发现方法[J]. 物理学报, 2013, 62(14):140101 HU Qingcheng, YIN Yanshen, MA Pengfei, et al. A new approach to identify influential spreaders in complex networks[J]. Acta physica sinica, 2013, 62(14):140101
[19] 蔡文, 杨春燕, 陈文伟, 等. 可拓集与可拓数据挖掘[M]. 北京:科学出版社, 2008:6.
[20] 杨春燕. 可拓创新方法[M]. 北京:科学出版社, 2017:3.
[21] 蔡文, 杨春燕. 可拓学的基础理论与方法体系[J]. 科学通报, 2013, 58(13):1190-1199 CAI Wen, YANG Chunyan. Basic theory and methodology on Extenics[J]. Chinese science bulletin, 2013, 58(13):1190-1199

相似文献/References:

[1]王 龙,伏 锋,陈小杰,等.复杂网络上的群体决策[J].智能系统学报,2008,3(02):95.
 WANG Long,FU Feng,CHEN Xiao-jie,et al.Collective decision-making over complex networks[J].CAAI Transactions on Intelligent Systems,2008,3(05):95.
[2]夏承遗,刘忠信,陈增强,等.复杂网络上的传播动力学及其新进展[J].智能系统学报,2009,4(05):392.[doi:10.3969/j.issn.1673-4785.2009.05.002]
 XIA Cheng-yi,LIU Zhong-xin,CHEN Zeng-qiang,et al.Transmission dynamics in complex networks[J].CAAI Transactions on Intelligent Systems,2009,4(05):392.[doi:10.3969/j.issn.1673-4785.2009.05.002]
[3]李伟,杨晓峰,张重阳,等.复杂网络社团的投影聚类划分[J].智能系统学报,2011,6(01):57.
 LI Wei,YANG Xiaofeng,ZHANG Chongyang,et al.A clustering method for community detection on complex networks[J].CAAI Transactions on Intelligent Systems,2011,6(05):57.
[4]孙世温,夏承遗,王莉.基于复杂网络的软件结构度量方法综述[J].智能系统学报,2011,6(03):208.
 SUN Shiwen,XIA Chengyi,WANG Li.Survey of the measurement of software structures based on complex networks[J].CAAI Transactions on Intelligent Systems,2011,6(05):208.
[5]赵敬,夏承遗,孙世温,等.复杂网络上同时考虑感染延迟和非均匀传播的SIR模型[J].智能系统学报,2013,8(02):128.[doi:10.3969/j.issn.1673-4785.201210027]
 ZHAO Jing,XIA Chengyi,SUN Shiwen,et al.A novel SIR model with infection delay and nonuniform transmission in complex networks[J].CAAI Transactions on Intelligent Systems,2013,8(05):128.[doi:10.3969/j.issn.1673-4785.201210027]
[6]仇建平,陈立潮,潘理虎.牵制控制下复杂网络的同步性研究[J].智能系统学报,2014,9(06):734.[doi:10.3969/j.issn.1673-4785.201311014]
 QIU Jianping,CHEN Lichao,PAN Lihu.Synchronization in complex networks via pinning control[J].CAAI Transactions on Intelligent Systems,2014,9(05):734.[doi:10.3969/j.issn.1673-4785.201311014]
[7]刘富,姜奕含,邹青宇.复杂网络结构比对算法研究进展[J].智能系统学报,2015,10(04):508.[doi:10.3969/j.issn.1673-4785.201408006]
 LIU Fu,JIANG Yihan,ZOU Qingyu.Advances in algorithms for construction alignment of complex networks research[J].CAAI Transactions on Intelligent Systems,2015,10(05):508.[doi:10.3969/j.issn.1673-4785.201408006]
[8]晁永翠,纪志坚,王耀威,等.复杂网络在路形拓扑结构下可控的充要条件[J].智能系统学报,2015,10(04):577.[doi:10.3969/j.issn.1673-4785.201411031]
 CHAO Yongcui,JI Zhijian,WANG Yaowei,et al.Necessary and sufficient conditions for the controllability of complex networks with path topology[J].CAAI Transactions on Intelligent Systems,2015,10(05):577.[doi:10.3969/j.issn.1673-4785.201411031]
[9]王景丽,许立波,庞超逸.复杂网络中的在线社交网络演化模型[J].智能系统学报,2015,10(6):949.[doi:10.11992/tis.201507042]
 WANG Jingli,XU Libo,PANG Chaoyi.Evolution model of online social networks based on complex networks[J].CAAI Transactions on Intelligent Systems,2015,10(05):949.[doi:10.11992/tis.201507042]
[10]郑文萍,张浩杰,王杰.基于稠密子图的社区发现算法[J].智能系统学报,2016,11(3):426.[doi:10.11992/tis.201603045]
 ZHENG Wenping,ZHANG Haojie,WANG Jie.Community detection algorithm based on dense subgraphs[J].CAAI Transactions on Intelligent Systems,2016,11(05):426.[doi:10.11992/tis.201603045]
[11]闫玲玲,陈增强,张青.基于度和聚类系数的中国航空网络重要性节点分析[J].智能系统学报,2016,11(5):586.[doi:10.11992/tis.201601024]
 YAN Lingling,CHEN Zengqiang,ZHANG Qing.Analysis of key nodes in China’s aviation network basedon the degree centrality indicator and clustering coefficient[J].CAAI Transactions on Intelligent Systems,2016,11(05):586.[doi:10.11992/tis.201601024]

备注/Memo

备注/Memo:
收稿日期:2018-11-20。
基金项目:国家自然科学基金项目(61572022);浙江省自然科学基金项目(LY16G010010,LY18F020001).
作者简介:严家萌,男,1987年生,硕士研究生,主要研究方向为数据挖掘、深度学习;许立波,男,1976年生,讲师,博士,主要研究方向为人工智能、智能信息处理;李兴森,男,1968年生,教授,博士,中国人工智能学会理事,中国人工智能学会可拓工程专业委员会副主任、秘书长,浙江省创造学研究会常务理事,国际MCDM、IEEE学会会员,主要研究方向为可拓学、智能知识管理与数据挖掘。承担及参加省部级以上项目8项,其中国家级项目6项。发表学术论文60余篇,出版专著2部。
通讯作者:许立波.E-mail:Xu_libo@163.com
更新日期/Last Update: 1900-01-01