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GraphLSHC: Towards large scale spectral hypergraph clustering
Yiyang Yang1; Sucheng Deng2; Juan Lu3; Yuhong Li4; Zhiguo Gong2; Leong Hou U2; Zhifeng Hao1,5
Source PublicationInformation Sciences

Hypergraph is popularly used for describing multi-relationships among objects in a unified manner, and spectral clustering is regarded as one of the most effective algorithms for partitioning those objects (vertices) into different communities. However, the traditional spectral clustering for hypergraph (HC) incurs expensive costs in terms of both time and space. In this paper, we propose a framework called GraphLSHC to tackle the scalability problem faced by the large scale hypergraph spectral clustering. In our solution, the hypergraph used in GraphLSHC is expanded into a general format to capture complicated higher-order relationships. Moreover, GraphLSHC is capable to simultaneously partition both vertices and hyperedges according to the “eigen-trick”, which provides an approach for reducing the computational complexity of the clustering. To improve the performance further, several hyperedge-based sampling techniques are proposed, which can supplement the sampled matrix with the whole graph information. We also give a theoretical guarantee for the error boundary of the supplement. Several experiments show the superiority of the proposed framework over the state-of-the-art algorithms.

KeywordClustering Hypergraph Machine Learning Unsupervised Learning
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000579455200007
Scopus ID2-s2.0-85088920131
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Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Faculty of Science and Technology
Corresponding AuthorZhiguo Gong
Affiliation1.Guangdong University of Technology,Faculty of Computer,China
2.State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science,University of Macau,Macao,Macao
3.Beijing Institute of Petrochemical Technology,Information Engineering,China
4.Alibaba Group,Security Department,China
5.Foshan University,School of Mathematics and Big Data,China
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yiyang Yang,Sucheng Deng,Juan Lu,et al. GraphLSHC: Towards large scale spectral hypergraph clustering[J]. Information Sciences,2021,544:117-134.
APA Yiyang Yang,Sucheng Deng,Juan Lu,Yuhong Li,Zhiguo Gong,Leong Hou U,&Zhifeng Hao.(2021).GraphLSHC: Towards large scale spectral hypergraph clustering.Information Sciences,544,117-134.
MLA Yiyang Yang,et al."GraphLSHC: Towards large scale spectral hypergraph clustering".Information Sciences 544(2021):117-134.
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