[1]方莲娣,张燕平,陈洁,等.基于三支决策的非重叠社团划分[J].智能系统学报,2017,12(03):293-300.[doi:10.11992/tis.201705013]
 FANG Liandi,ZHANG Yanping,CHEN Jie,et al.Three-way decision based on non-overlapping community division[J].CAAI Transactions on Intelligent Systems,2017,12(03):293-300.[doi:10.11992/tis.201705013]
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基于三支决策的非重叠社团划分(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第12卷
期数:
2017年03期
页码:
293-300
栏目:
出版日期:
2017-06-25

文章信息/Info

Title:
Three-way decision based on non-overlapping community division
作者:
方莲娣12 张燕平12 陈洁12 王倩倩3 刘峰12 王刚12
1. 安徽大学 计算机科学与技术学院, 安徽 合肥 230601;
2. 安徽大学 计算机智能与信号处理教育部重点实验室, 安徽 合肥 230601;
3. 安徽大学 国际商学院, 安徽 合肥 230601
Author(s):
FANG Liandi12 ZHANG Yanping12 CHEN Jie12 WANG Qianqian3 LIU Feng12 WANG Gang12
1. School of Computer Science and Technology, Anhui University, Hefei 230601, China;
2. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China;
3. School of Business, Anhui University, Hefei 230601, China
关键词:
复杂网络社团划分重叠节点三支决策理论粒化系数层次聚类社团结构节点归属度
Keywords:
complex networkcommunity divisionoverlapping nodethree-way decisiongranulation coefficienthierarchical clusteringcommunity structurenode belonging degree
分类号:
TP301
DOI:
10.11992/tis.201705013
摘要:
基于三支决策理论,提出了一种基于三支决策的非重叠社团划分算法(N-TWD),该方法将初始聚类形成的重叠社团进行二次划分以形成最终的非重叠社团。N-TWD算法首先利用层次聚类形成有重叠的社团结构,将两个存在重叠的社团的左边社团中非重叠部分定义为正域,右边社团中非重叠部分定义为负域,而两个社团的重叠部分定义为边界域。然后,针对边界域中的节点,分别计算边界域中节点与正域和负域的社团归属度BPBN进行二次划分。对于二次划分后仍然留在边界域中的节点将利用投票的方法决定其最终归属,最终获得非重叠的社团结构。本文选取4个经典社交网络数据集和1个真实世界数据集对N-TWD算法进行了验证,相比较其他社团划分算法(GN、NFA、LPA、CACDA),N-TWD时间复杂度较低,总体获取的社团模块度值更高。
Abstract:
This paper proposes an algorithm called N-TWD based on the theory of three-way decision, which can further divide overlapping communities formed by the initial clustering into non-overlapping communities. First, it utilizes a hierarchical clustering algorithm to get an overlapping community structure. The nodes in the non-overlapping parts of the community of the left side between two communities with overlapping parts were defined as positive regions. Then, the nodes on its right are denoted as the negative region, and nodes in the overlapping parts are denoted as the boundary region. The degree of belonging (BP,BN) between the positive and negative regions was calculated using the nodes in the boundary region. Moreover, a further division was done based on the degree of belonging. After division, the belonging of the rest nodes in the boundary region would be determined by voting to ultimately get a non-overlapping community structure. The experimental results for four classical social networks and one real-world data-set indicate that the proposed algorithm has a lower time complexity and gets a higher modularity value than other community division algorithms (GN, NFA, LPA, CACDA).

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备注/Memo

备注/Memo:
收稿日期:2017-05-12。
基金项目:国家“863”计划项目(2015AA124102);国家自然科学基金项目(61673020,61602003,61402006);安徽省自然科学基金项目(1508085MF113,1708085QF156,1708085QF143,1708085MF163);安徽省高等学校省级自然科学基金重点项目(KJ2013A016,KJ2016A016);教育部人文社科青年基金项目(14YJC860020).
作者简介:方莲娣,女,1992年生,硕士研究生,主要研究领域为三支决策和机器学习;张燕平,女,1962年生,教授,博士,主要研究方向为粒计算、智能计算、机器学习。获发明专利2项,发表学术论文80余篇;陈洁,女,1982年生,副教授,博士,主要研究方向为智能计算、机器学习、三支决策。发表学术论文10余篇。
通讯作者:张燕平.E-mail:zhangyp2@gmail.com.
更新日期/Last Update: 2017-06-25