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Community Discovery in Social Networks via Heterogeneous Link Association and Fusion


Authors: L. Meng and A.-H. Tan
Title: Community Discovery in Social Networks via Heterogeneous Link Association and Fusion
Abstract: 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 prior, do not address the weighting problem for fusing heterogeneous types of links and have a heavy computational cost. In this paper, we explore the feasibility of a newly proposed heterogeneous data clustering algorithm, called Generalized Heterogeneous Fusion Adaptive Resonance Theory (GHF-ART), for discovering user communities in social networks. Different from existing algorithms, GHF-ART performs real-time matching of patterns and one-pass learning which guarantee its low computational cost. With a vigilance parameter to restrain the intra-cluster similarity, GHF-ART does not need the number of clusters a prior. To achieve a better fusion of multiple types of links, GHF-ART employs a weighting function to incrementally assess the importance of all the feature channels. Extensive experiments have been conducted to analyze the performance of GHF-ART on two heterogeneous social network data sets. The promising results comparing with existing methods demonstrate the effectiveness and efficiency of GHF-ART.
Keywords: User community discovery; Heterogeneous social networks; Heterogeneous data clustering; Multimodal feature weighting
Conference Name: SIAM International Conference on Data Mining (SDM'14)
Location: Philadelphia, USA
Publisher: SIAM
Year: 2014
Accepted PDF File: Community_Discovery_in_Social_Networks_via_Heterogeneous_Link_Association_and_Fusion_accepted.pdf
Permanent Link: http://dx.doi.org/10.1137/1.9781611973440.92
Reference: L. Meng and A.-H. Tan, “Community discovery in social networks via heterogeneous link association and fusion,” in Proceedings of the SIAM International Conference on Data Mining (SDM’14). SIAM, April 2014, pp. 803–811.
bibtex: 
@inproceedings{LILY-c15,
   author 	= {Meng, Lei and Tan, Ah-Hwee},
   title 	= {Community Discovery in Social Networks via Heterogeneous Link Association and Fusion},
   booktitle 	= {Proceedings of the SIAM International Conference on Data Mining (SDM'14)},
   year 	= {2014},
   month	= {April}, 
   pages 	= {803-811},
   location 	= {Philadelphia, USA},
   publisher	= {SIAM},
}