Publication Type
Book Chapter
Version
publishedVersion
Publication Date
5-2019
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 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 [11] (Copyright ©2014 Society for Industrial and Applied Mathematics. Reprinted with permission. All rights reserved).
Discipline
Databases and Information Systems | Digital Communications and Networking
Research Areas
Data Science and Engineering
Publication
Adaptive Resonance Theory in Social Media Data Clustering
First Page
137
Last Page
154
ISBN
9783030029852
Identifier
10.1007/978-3-030-02985-2_6
Publisher
Springer
City or Country
Cham
Citation
MENG, Lei; TAN, Ah-hwee; and WUNSCH, Donald C..
Community discovery in heterogeneous social networks. (2019). Adaptive Resonance Theory in Social Media Data Clustering. 137-154.
Available at: https://ink.library.smu.edu.sg/sis_research/6063
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-030-02985-2_6
Included in
Databases and Information Systems Commons, Digital Communications and Networking Commons