dc.citation.conferencePlace |
AT |
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dc.citation.conferencePlace |
Virtual |
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dc.citation.endPage |
2147 |
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dc.citation.startPage |
2134 |
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dc.citation.title |
International Conference on Very Large Data Bases |
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dc.contributor.author |
Kim, Junghoon |
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dc.contributor.author |
Feng, Kaiyu |
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dc.contributor.author |
Cong, Gao |
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dc.contributor.author |
Zhu, Diwen |
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dc.contributor.author |
Yu, Wenyuan |
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dc.contributor.author |
Miao, Chunyan |
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dc.date.accessioned |
2024-01-31T20:06:38Z |
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dc.date.available |
2024-01-31T20:06:38Z |
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dc.date.created |
2022-09-11 |
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dc.date.issued |
2022-09-05 |
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dc.description.abstract |
Finding a set of co-clusters in a bipartite network is a fundamental and important problem. In this paper, we present the Attributed Bipartite Co-clustering (ABC) problem which unifies two main concepts: (i) bipartite modularity optimization, and (ii) attribute cohesiveness. To the best of our knowledge, this is the first work to find co-clusters while considering the attribute cohesiveness. We prove that ABC is NP-hard and is not in APX, unless P=NP. We propose three algorithms: (1) a top-down algorithm; (2) a bottom-up algorithm; (3) a group matching algorithm. Extensive experimental results on real-world attributed bipartite networks demonstrate the efficiency and effectiveness of our algorithms. |
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dc.identifier.bibliographicCitation |
International Conference on Very Large Data Bases, pp.2134 - 2147 |
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dc.identifier.doi |
10.14778/3547305.3547318 |
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dc.identifier.issn |
2150-8097 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/75514 |
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dc.language |
영어 |
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dc.publisher |
Association for Computing Machinery (ACM) |
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dc.title |
ABC : Attributed Bipartite Co-clustering |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2022-09-05 |
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