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...

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Main Authors: MENG, Lei, TAN, Ah-hwee, WUNSCH, Donald C.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/sis_research/6063
https://ink.library.smu.edu.sg/context/sis_research/article/7066/viewcontent/454069_1_En_Print.indd.pdf
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spelling sg-smu-ink.sis_research-70662023-08-11T03:18:39Z Community discovery in heterogeneous social networks MENG, Lei TAN, Ah-hwee WUNSCH, Donald C. 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). 2019-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6063 info:doi/10.1007/978-3-030-02985-2_6 https://ink.library.smu.edu.sg/context/sis_research/article/7066/viewcontent/454069_1_En_Print.indd.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Digital Communications and Networking
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Digital Communications and Networking
spellingShingle Databases and Information Systems
Digital Communications and Networking
MENG, Lei
TAN, Ah-hwee
WUNSCH, Donald C.
Community discovery in heterogeneous social networks
description 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).
format text
author MENG, Lei
TAN, Ah-hwee
WUNSCH, Donald C.
author_facet MENG, Lei
TAN, Ah-hwee
WUNSCH, Donald C.
author_sort MENG, Lei
title Community discovery in heterogeneous social networks
title_short Community discovery in heterogeneous social networks
title_full Community discovery in heterogeneous social networks
title_fullStr Community discovery in heterogeneous social networks
title_full_unstemmed Community discovery in heterogeneous social networks
title_sort community discovery in heterogeneous social networks
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/6063
https://ink.library.smu.edu.sg/context/sis_research/article/7066/viewcontent/454069_1_En_Print.indd.pdf
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