Community detection of Chinese micro-blogging using multi-dimensional weighted network

被引：0

作者：

Zhou, Xiaoping ^{[1
,2
]}

Liang, Xun ^{[1
]}

Cao, Run ^{[1
]}

机构：

[1] Renmin University of China, Beijing

[2] Beijing University of Civil Engineering and Architecture, Beijing

来源：

Open Cybernetics and Systemics Journal | 2014年 / 8卷 / 01期

关键词：

Chinese; CNM; Community detection; Micro-blogging; Multi-dimensional; Weighted network;

D O I：

10.2174/1874110X01408011188

中图分类号：

学科分类号：

摘要：

Existing community detection methods are mostly based on the analysis of the links among the nodes, ignoring the rich, while the others often ignore the network structure which is the foundation of social media. Aiming at the existed problems, this paper proposed a community detection algorithm based on multi-dimensional weighted network. By introducing User Interactive Frequency, User Interest Similarity, and User Attributes Similarity into the basic network topology, a multi-dimensional weighted network is set up. After converting the multi-dimensional weighted network into a single- dimensional weighted network, an improved CNM algorithm is exploited to discover the communities. A corresponding series of evaluation indicators are proposed to evaluate the detection results. By evaluating the algorithm in the dataset of Chinese Micro-blogging, it reveals that the clustering results are better when extra information is used, and in Chinese Micro-blogging platform, User Interactive Frequency plays a much more important role in community detection. © Zhou et al.

引用

页码：1188 / 1197

页数：9

共 9 条

[1]

Newman M.E.J., Girvan M., Finding and evaluating community structure in networks, Physical Review E, 69, 2, (2004)

[2]

White S., Smyth P., A spectral clustering approach to finding communities in graph, Proceedings of the Fifth SIAM International Conference on Data Mining, pp. 274-285, (2005)

[3]

Palla G., Derenyi I., Farkas I., Vicsek T., Uncovering the overlapping community structure of complex networks in nature and society, Nature, 435, pp. 814-818, (2005)

[4]

He X., Zha H., Ding C.H.Q., Simon H.D., Web document clustering using hyperlink structures, Computational Statistics and Data Analysis, 41, 1, pp. 19-45, (2002)

[5]

Steyvers M., Smyth P., Rosen-Zvi M., Griffiths T., Probabilistic author-topic models for information discovery, The Proceeding of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 306-315, (2004)

[6]

Zhou D., Councill I., Zha H., Giles C., Discovering temporal communities from social network documents, Proceedings of the 7Th IEEE International Conference on Data Mining, pp. 745-750, (2007)

[7]

Yan F., Zhang M., Tan Y.W., Tang J., Deng Z.H., Community discovery based on actorsinterests and social network structure, Journal of Computer Research and Development, 47, pp. 357-362, (2010)

[8]

Java A., Song X.D., Finin T., Tseng B., Why We Twitter: Understanding Microblogging Usage and Communities, Proceedings of the 9Th Webkdd and 1St SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56-65, (2007)

[9]

Zhang Z.F., Li Q.D., Zeng D., Gao H., User community discovery from multi-relational networks, Decision Support Systems, 54, 2, pp. 870-879, (2013)

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