[1]王伟,李玲娟. 一种基于聚类的社团划分算法[J].计算机技术与发展,2015,25(10):119-122.
　WANG Wei,LI Ling-juan. A Clustering-based Community Division Algorithm[J].,2015,25(10):119-122.
点击复制

 一种基于聚类的社团划分算法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年10期
页码:
119-122
栏目:
智能、算法、系统工程
出版日期:
2015-10-10

文章信息/Info

Title:
 A Clustering-based Community Division Algorithm
文章编号:
1673-629X（2015)10-0119-04
作者:
 王伟; 李玲娟
 南京邮电大学 计算机学院
Author(s):
 WANG Wei; LI Ling-juan
关键词:
 ; 社会网络; 社团划分; 聚集系数; 相似性; 聚类
Keywords:
 ; social network; community division; clustering coefficient; similarity clustering
分类号:
TP311
文献标志码:
A
摘要:
 社团划分是社会网络的一个研究热点。为了快速准确地发现社会网络的社团结构,文中从节点的重要度出发,利用节点之间的相似性,提出了一种基于聚类的社团划分算法—CCDA。其基本思想是每次以节点集合中聚集系数最大的点作为聚类中心,基于最短路径和欧几里得距离计算节点相似度,选择与聚类中心的相似度大于给定阈值的点进行聚类,不断迭代,直至节点集合为空,所产生的各个簇即为不同的社团。对被重复划分的节点,以模块度函数为标准,将节点归属到最合适的社团中。由于该算法每次从重要节点出发,再次选取聚类中心时不需考虑已经被聚类的节点,所以时间复杂度低于GN算法和Newman算法。将该算法应用于经典的社会网络Zachary,结果表明了CCDA算法对社团划分的有效性。
Abstract:
 Community division has been a research focus in the social network area. In order to quickly and accurately find community structure in the social network,from the importance of nodes and consulting their similarities,propose a clustering-based community divi-sion algorithm CCDA. The basic idea of this algorithm is selecting the node owning greater clustering coefficient as the clustering center, calculating similarity by the shortest path and Euclidean distance,putting the node with similarity greater than given threshold to cluster, and iterating the process until the node collection is empty. For the repeated division nodes,the algorithm divides each of them into the most appropriate community by using the module function Q. The clusters generated by the algorithm are corresponding with the commu-nities. Since the algorithm starts from the important node and does not consider those clustered nodes when determining new clustering center,the time complexity of it is lower than GN algorithm and Newman algorithm. The results of applying the algorithm to the classical social network,the Zachary network,show that CCDA is valid in community division.

相似文献/References:

[1]陈加忠 鞠增伟 陈常念 李榕 夏涛 王冼.基于社会网络特性的机会计算服务平台[J].计算机技术与发展,2011,(06):197.
　CHEN Jia-zhong,JU Zeng-wei,CHEN Chang-nian,et al.Opportunistic Computing Service Platform Based on Social Network Properties[J].,2011,(10):197.
[2]殷凤霞.社会网络中基于内容语义的新闻推荐方法研究[J].计算机技术与发展,2013,(10):253.
　YIN Feng-xia.Research on Method of News Recommendation Based on Content Semantic in Social Network[J].,2013,(10):253.
[3]王庚,宋传超,盛玉晓,等.基于标签传播的社区挖掘算法研究综述[J].计算机技术与发展,2013,(12):69.
　WANG Geng,SONG Chuan-chao,SHENG Yu-xiao,et al.Research Summary on Communities Mining Algorithm Based on Label Propagation[J].,2013,(10):69.
[4]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(10):1.
[5]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(10):5.
[6]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(10):13.
[7]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(10):21.
[8]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(10):25.
[9]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(10):29.
[10]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(10):34.

更新日期/Last Update: 2015-11-13