Computer Science ›› 2017, Vol. 44 ›› Issue (Z11): 385-390.doi: 10.11896/j.issn.1002-137X.2017.11A.081

Previous Articles     Next Articles

Clustering and Visualization of Social Network Based on User Interests

TANG Ying, ZHONG Nan-jiang, SUN Kang-gao, QIN Da-kang and ZHOU Wei-hua   

  • Online:2018-12-01 Published:2018-12-01

Abstract: With the development of social network,it becomes more and more important to extract useful information from the social network and provide valuable knowledge to users in an interactive visual interface intuitively.Clustering,as a crucial method in data mining,offers the global data analysis results.Traditional clustering methods of social network data mainly consider network topological structure.However, they haven’t considered the user interests for clustering.In this paper,the users are clustered by computing user-interest similarity based on Bayesian probabilistic model,furthermore,the interactive visualization method is designed to present the user clustering results.Specifically,we computed the feature vectors representing users’ interests based on latent semantic model.Then clusters with different interest characteristics were built based on these feature vectors.The suitable number of clusters are determined by heat map visualization results.Finally,we presented the interactive visualization method based on hierarchical bubble chart to support users to explore the clustering results from the global overview to local details.We performed experiments and analysis with data crawled from Douban website.The results validate the effectiveness of our method.

Key words: Social network,Clustering,Data visualization,Latent semantic model

[1] GOU L,YOU F,GUO J,et al.Sfviz:interest-based friends exploration and recommendation in social networks[C]∥Procee-dings of the 2011 Visual Information Communication-InternationalSymposium.ACM,2011:15.
[2] KRULWICH B.Lifestyle finder:intelligent user profiling using large-scale demographic data[J].Artificial Intelligence Magazine,1997,18(2):37-45.
[3] HOFMANN T.Latent semantic models for collaborative filte-ring[J].ACM Transactions on Information Systems (TOIS),2004,22(1):89-115.
[4] HOFMANN T.Probabilistic latent semantic indexing[C]∥Proceedings of the 22nd Annual International ACM SIGIR Con-ference on Research and Development in Information Retrieval.ACM,1999:50-57.
[5] GOLUB G H,REINSCH C.Singular value decomposition andleast squares solutions[J].Numerische Mathematik,1970,14(5):403-420.
[6] WOLD S,ESBENSEN K,GELADI P.Principal component ana-lysis[J].Chemometrics and Intelligent Laboratory Systems,1987,2(1-3):37-52.
[7] YE M,LIU X,LEE W C.Exploring social influence for recommendation:a generative model approach[C]∥Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2012:671-680.
[8] CARD S K,MACKINLAY J D,SHNEIDERMAN B.Readings in Information Visualization:Using Vision To Think[M].San Francisco:MorganKaufmann Publishers,1999:1-712.
[9] HERMAN I,MELANCON G,MARSHALL M S.Graph visuali-zation and navigation in information visualization:A survey[J].IEEE Trans.On Visualization and Computer Graphics,2000,6(1):24-43.
[10] JOHNSON B,SHNEIDERMAN B.Tree-maps:a space-fillingapproach to the visualization of hierarchical information structures[C]∥IEEE Conference on Visualization’91.IEEE,1991:284-291.
[11] JOHNSON B,SHNEIDERMAN B.Tree-maps:a space-fillingapproach to the visualization of hierarchical information structures[C]∥IEEE Conference on Visualization’91.IEEE,1991:284-291.
[12] BALZER M,DEUSSEN O,LEWERENTZ C.Voronoi treemaps for the visualization of software metrics.[C]∥Proceedings of the 2005 ACM Symposium on Software Visualization.New York:ACM,2005:165-172.
[13] FRIENDLY M.A brief history of the mosaic display[J].Journal of Computational and Graphical Statistics,2002,11(1):89-107.
[14] WANG W,WANG H,DAI G,et al.Visualization of large hierar-chical data by circle packing[C]∥Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.New York:ACM,2006:517-520.
[15] KEIM D,ANDRIENKO G,FEKETE J D,et al.Visual analy-tics:Definition,process,and challenges[M].Springer Berlin Heidelberg,2008.
[16] PIKE W A,STASKO J,CHANG R,et al.The science of interac-tion[J].Information Visualization,2009,8(4):263-274.
[17] KAUFMAN L,ROUSSEEUW P J.Finding groups in data:anintroduction to cluster analysis[M].John Wiley & Sons,2009.
[18] EICK S G.Graphically displaying text[J].Journal of Computational and Graphical Statistics,1994,3:127-142.
[19] STASKO J.Information visualization. classes /AY2004/cs7450_spring.
[20] FENG Y D,WANG G P,DONG S H.Information Visualization[J].Journal of Engineering Graphics,2001:324-329.
[21] SMITH M A,SHNEIDERMAN B,M ILIC-FRAYLIN N,et al.Analyzing (social media) networks with NodeXL[C]∥Procee-dings of the Fourth International Conference on Communities and Technologies.ACM,2009:255-264.
[22] HENRY N,FEKETE J D.MatrixExplorer:a Dual-Representation System to Explore Social Networks[J].IEEE Transactions on Visualization & Computer Graphics,2006,12(5):677-684.
[23] FRUCHTERMANN T M J,REINGOLD E M.Graph drawing by force-directed placement[J].Software:Practice andexperien-ce,1991,21(11):1129-1164.

No related articles found!
Full text



No Suggested Reading articles found!