[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(5):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(5):915-921.[doi:10.11992/tis.201811012]
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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(5):915-921.[doi:10.11992/tis.201811012]
可拓聚类的科教人际网络节点重要性动态分析方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第5期
页码:
915-921
栏目:
学术论文—智能系统
出版日期:
2019-09-05
- Title:
- Dynamic analysis method of importance of science and education interpersonal network nodes based on extension clustering
- Keywords:
- complex network; node importance; multi-attribute; extenics; extension clustering; extension theory; matter element; correlation function
- 分类号:
- TP301.6
- 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.
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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