Residential College | false |
Status | 已發表Published |
GraphLSHC: Towards large scale spectral hypergraph clustering | |
Yiyang Yang1; Sucheng Deng2; Juan Lu3; Yuhong Li4; Zhiguo Gong2; Leong Hou U2; Zhifeng Hao1,5 | |
2021-01-12 | |
Source Publication | Information Sciences |
ISSN | 0020-0255 |
Volume | 544Pages:117-134 |
Abstract | 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. |
Keyword | Clustering Hypergraph Machine Learning Unsupervised Learning |
DOI | 10.1016/j.ins.2020.07.018 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Computer Science |
WOS Subject | Computer Science, Information Systems |
WOS ID | WOS:000579455200007 |
Publisher | ELSEVIER SCIENCE INCSTE 800, 230 PARK AVE, NEW YORK, NY 10169 |
Scopus ID | 2-s2.0-85088920131 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | THE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU) Faculty of Science and Technology DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE |
Corresponding Author | Zhiguo Gong |
Affiliation | 1.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 Affilication | University 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. |
Files in This Item: | There are no files associated with this item. |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment