[1]封红旗,雷晨阳,沈田予,等.基于改进完全子图模型的关注对象多社区发现研究[J].南京理工大学学报(自然科学版),2016,40(06):674.[doi:10.14177/j.cnki.32-1397n.2016.40.06.006 ]
 Feng Hongqi,Lei Chenyang,Shen Tianyu,et al.Concerned objects multi-community detection based on improved complete subgraph model[J].Journal of Nanjing University of Science and Technology,2016,40(06):674.[doi:10.14177/j.cnki.32-1397n.2016.40.06.006 ]
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基于改进完全子图模型的关注对象多社区发现研究
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
40卷
期数:
2016年06期
页码:
674
栏目:
出版日期:
2016-12-30

文章信息/Info

Title:
Concerned objects multi-community detection based on improved complete subgraph model
文章编号:
1005-9830(2016)06-0674-05
作者:
封红旗雷晨阳沈田予杨长春
常州大学 信息科学与工程学院,江苏 常州 213164
Author(s):
Feng HongqiLei ChenyangShen TianyuYang Changchun
School of Information Science and Engineering,Changzhou University,Changzhou 213164,China
关键词:
完全子图模型 关注对象 多类 阈值划分 数据挖掘算法
Keywords:
complete subgraph model concerned objects multi-classification threshold division data mining algorithm
分类号:
TP31
DOI:
10.14177/j.cnki.32-1397n.2016.40.06.006
摘要:
为实现用户和关注对象的多社区划分,针对完全子图模型不能进行多类分类的缺陷,该文引入了阈值划分方法,提出基于改进完全子图模型的社区发现算法。实验表明:与经典数据挖掘算法K-medoids相比,该文算法具有更高的准确性。
Abstract:
A multi-community detection method based on improved complete subgraph model is proposed using threshold division for multi-community division of users and concerned objects,because complete subgraph model cannot divide users and concerned objects based on multi-classification.Experiment result shows that compared with classical data mining algorithm K-medoids,this method is more accurate.

参考文献/References:

[1] Grivan M,Newman M E J.Community structure in social and biological network[J].Proceedings of the National Academy of Sciences of the United States of America,2002,99(12):7821-7826.
[2]Newman M E.Fast algorithm for detecting community structure in networks[J].Physical Review E,Statistical,Nonlinear,and Soft Matter Physics,2004,69(6):066133-066133.
[3]Wu Fang,Huberman B A.Finding communities in linear time:A physics approach[J].The European Physical Journal B,2004,38(2):331-338.
[4]Zhang Huiqi,Dantu R.Discovery of social groups using call detail records[J].Lecture Notes in Computer Science,2008,5333:489-498.
[5]Ye Conghuan.Dense subgroup identifying in social network[C]//2011 International Conference on Advances in Social Networks Analysis and Mining(ASONAM'11).Washington,DC,USA:IEEE Computer Society,2011:555-556
[6]Qu Zhonghua,Liu Yang.Interactive group suggesting for Twitter[EB/OL].http://www.aclweb.org/website/old_anthology/P/P11/P11-2091.pdf,2016-11-09.
[7]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):426-432.
Zheng Wenping,Zhang Haojie,Wang Jie.Community detection algorithm based on dense subgraphs[J].CAAI Transactions on Intelligent Systems,2016,11(3):426-432.
[8]周小平,梁循,张海燕.基于R-C模型的微博用户社区发现[J].软件学报,2014(12):2808-2823.
Zhou Xiaoping,Liang Xun,Zhang Haiyan.User community detection on micro-blog using R-C model[J].Journal of Software,2014(12):2808-2823.
[9]汪涛,刘阳,席耀一.基于图正则化非负矩阵分解的二分网络社区发现算法[J].电子与信息学报,2015(9):2238-2245.
Wang Tao,Liu Yang,Xi Yaoyi.Identifying community in bipartite networks using graph regularized-based non-negative matrix factorization[J].Journal of Electronics & Information Technology,2015(9):2238-2245.
[10]康颖,古晓艳,于博,等.一种面向大规模社会信息网络的多层社区发现算法[J].计算机学报,2016(1):169-182.
Kang Ying,Gu Xiaoyan,Yu Bo,et al.A multilevel community detection algorithm for large-scale social information networks[J].Chinese Journal of Computers,2016(1):169-182.
[11] 封红旗,沈田雨,杨长春.社交网络通信目标检测优化仿真研究[J].计算机仿真,2015,32(12):164-167.
Feng Hongqi,Shen Tianyu,Yang Changchun.Simulation of mining method for special objects in social networks[J].Computer Simulation,2015,32(12):164-167.

备注/Memo

备注/Memo:
收稿日期:2016-07-03 修回日期:2016-10-23
基金项目:国家自然科学基金(61272367); 江苏省科技厅项目(BZ2010021); 江苏省研究生培养创新工程项目(20120515); 江苏省产学研前瞻性联合研究项目(BY2014037-08)
作者简介:封红旗(1967-),男,副研究员,主要研究方向:数据挖掘,E-mail:hqfeng@cczu.edu.cn。
引文格式:封红旗,雷晨阳,沈田予,等.基于改进完全子图模型的关注对象多社区发现研究[J].南京理工大学学报,2016,40(6):674-678.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2016-12-30