Community discovery in social networks via heterogeneous link association and fusion

• Main Authors: MENG, Lei, TAN, Ah-hwee
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6566; https://ink.library.smu.edu.sg/context/sis_research/article/7569/viewcontent/Community_Discovery_via_GHF_ART___SDM_2014.pdf
• Institution: Singapore Management University

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 communities in heterogeneous 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 and the promising results comparing with existing methods demonstrate the effectiveness and efficiency of GHF-ART.