Abstract
One fundamental task is to find communities in the field of social network analysis. The heterogeneous multi-mode social network, which contains different entities such as user–user link information and user metadata (content), further complicates the already challenging problem of community detection. Most existing studies focus solely on link structure or content information; few methods exploit both for community detection. Actually, neither link structure nor user content alone is satisfactory in determining accurately the memberships. This paper proposes a new community detection method for heterogeneous multi-mode social network in the framework of co-training to combine link and content analysis, which trains two classifiers with the help of Naive Bayesian and semi-supervised modularity maximization. Experimental results on real-world social media data show that the proposed framework significantly outperforms the approaches, which rely on link structure or content analysis alone.
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Acknowledgments
This work is supported by the National Science Foundation of China under Grant no.60902069, no.61171124, supported by Science Technology Planning Project of Guangdong (Grant No.2011B010200045). Supported by Science Technology Planning Project of Shenzhen (Grant No.JCYJ20130329110601621).
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Wang, N., Chen, P., Li, X. (2014). Community Detection in Heterogeneous Multi-mode Social Network via Co-training. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_50
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DOI: https://doi.org/10.1007/978-3-642-54924-3_50
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