|Table of Contents|

Social network clustering analysis based on structural approximation


Research Field:
Publishing date:


Social network clustering analysis based on structural approximation
Wang Yunye1Kong Shan1Li Yalun2
1.College of Information Science & Technology,Zhengzhou Normal University,Zhengzhou 450044,China; 2.School of Electronic and Information Engineering,Tianjin Polytechnic University,Tianjin 300387,China
social network directed graphs network clustering structural approximation
In view of that the clustering algorithm based on structural approximation can not solve the clustering problem of the asymmetric network,a directed clustering algorithm based on structural approximation is proposed here. The social network is studied as a graph structure,and the network clustering problem is regarded as a sub-graph division to realize the clustering of directed graphs with low complexity. The algorithm was achieved by C++programming,and the customized directed network datasets and standard datasets are used to test the proposed algorithm. The experimental results show that the algorithm for the network structure is more accurate and can identify the outliers and hub points.


[1] 李磊,汪萌,吴信东. 基于社交网络的社交行为分析[J]. 电子与信息学报,2017,32(6):116-121.
Li Lei,Wang Meng,Wu Xindong. Analysis of social behavior based on social network[J]. Journal of Electronics Information Technology,2017,32(6):116-121.
[2]Igor Etxabe,Jesús M Valdaliso. Measuring structural social capital in a cluster policy network:Insights from the basque country[J]. European Planning Studies,2016,24(2):245-251.
[3]潘理,吴鹏,黄丹华. 在线社交网络群体发现研究进展[J]. 电子与信息学报,2017,34(4):214-247.
Pan Li,Wu Peng,Huang Danhua. Research progress in online social network group discovery[J]. Journal of Electronics & Information Technology,2017,34(4):214-247.
[4]Newman M E J. Fast algorithm for detecting community structure in networks[J]. Physical Review E,2014,69(6):066133-1-066133-5.
[5]张中军,张文娟,于来行,等. 基于网络距离和内容相似度的微博社交网络社区划分方法[J]. 山东大学学报(理学版),2017,34(6):1409-1416.
Zhang Zhongjun,Zhang Wenjuan,Yu Laixing,et al. A community division method of Weibo social network based on network distance and content similarity[J]. Journal of Shandong University,2017,34(6):1409-1416.
[6]Sarah L Buglass,Jens F Binder,Lucy R Betts,et al. When‘friends'collide:Social heterogeneity and user vulnerability on social network sites[J]. Computers in Human Behavior,2016,54(3):3167-3173.
[7]陈季梦,陈佳俊,刘杰,等. 基于结构相似度的大规模社交网络聚类算法[J]. 电子与信息学报,2015,37(2):449-454.
Chen Jimeng,Chen Jiajun,Liu Jie,et al. A large-scale social network clustering algorithm based on structural similarity[J]. Journal of Electronics & Information Technology 2015,37(2):449-454.
[8]Lara Khansa,Christopher W Zobel,Guillermo Goicochea. Creating a taxonomy for mobile commerce innovations using social network and cluster analyses[J]. International Journal of Electronic commerce,2012,32(3):164-170.
[9]梁迪,崔靖,李翔. 线下交互的动态社交网络研究进展:挑战与展望[J]. 计算机学报,2017,65(5):1526-1531.
Liang Di,Cui Jing,Li Xiang. Research progress of dynamic social networks with offline interaction:Challenges and prospects[J]. Chinese Journal of Computers,2017,65(5):1526-1531.
[10]Niu Danmei,Rui Lanlan,Huang Haoqiu,et al. A service transmission and recovery strategy based on cluster in mobile social network service environment[J]. Recent Advances in Electrical & Electronic Engineering,2015,34(6):82-100.
[11]Cao Yan,Cao Jian,Li Minglu. Distributed data distribution mechanism in social network based on fuzzy clustering[J]. Foundations and Applications of Intelligent Systems,2014,213:603-620.
[12]龚卫华,陈彦强,裴小兵,等. LBSN中融合多维关系的社区发现方法[J]. 软件学报,2018,28(4):1163-1176.
Gong Weihua,Chen Yanqiang,Pei Xiaobing,et al. Community detection of multi-dimensional relationships in location-based social networks[J]. Journal of Software,2018,28(4):1163-1176.
[13]Fallani F D V,Nicosia V,Latora V,et al. Nonparametric resampling of random walks for spectral network clustering[J]. Physical Review E,2014,89(1):012802-1-012802-5.
[14]Lancichinetti A,Fortunato S,Kertész J. Detecting the overlapping and hierarchical community structure in complex networks[J]. New Journal of Physics,2015,11(3):033015-1-033015-18.
[15]孙云云,江朝晖,单桂朋,等. 最优距离聚类和特征融合表达的关键帧提取[J]. 南京理工大学学报,2018,42(4):416-423.
Sun Yunyun,Jiang Zhaohui,Shan Guipeng,et al. Key frame extraction based on optimal distance clustering and feature fusion expression[J]. Journal of Nanjing University of Science and Technology,2018,42(4):416-423.


Last Update: 2020-04-20