Community Discovery in Heterogeneous Social Networks
Discovering social communities of web users through clustering analysis of heterogeneous link associations has drawn much attention. However, existing approaches typically require the number of clusters a priori, do not address the weighting problem for fusing heterogeneous types of links, and have a heavy computational cost. This chapter studies the commonly used social links of users and explores the feasibility of the proposed heterogeneous data co-clustering algorithm GHF-ART, as introduced in Sect. 3.6, for discovering user communities in social networks. Contrary to the existing algorithms proposed for this task, GHF-ART performs real-time matching of patterns and one-pass learning, which guarantees its low computational cost. With a vigilance parameter to restrain the intra-cluster similarity, GHF-ART does not need the number of clusters a priori. To achieve a better fusion of multiple types of links, GHF-ART employs a weighting algorithm, called robustness measure (RM), to incrementally assess the importance of all the feature channels for the representation of data objects of the same class. Extensive experiments have been conducted on two social network datasets to analyze the performance of GHF-ART. The promising results compare GHF-ART with existing methods and demonstrate the effectiveness and efficiency of GHF-ART. The content of this chapter is summarized and extended from 
L. Meng et al., "Community Discovery in Heterogeneous Social Networks," Advanced Information and Knowledge Processing, pp. 137-154, Springer London, May 2019.
The definitive version is available at https://doi.org/10.1007/978-3-030-02985-2_6
Electrical and Computer Engineering
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01 May 2019