ABC: attributed bipartite co-clustering

ABC: attributed bipartite co-clustering

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 knowl...

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Veröffentlicht in:Proceedings of the VLDB Endowment 2022-06, Vol.15 (10), p.2134-2147
Hauptverfasser: Kim, Junghoon, Feng, Kaiyu, Cong, Gao, Zhu, Diwen, Yu, Wenyuan, Miao, Chunyan
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container_issue 10
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creator Kim, Junghoon
Feng, Kaiyu
Cong, Gao
Zhu, Diwen
Yu, Wenyuan
Miao, Chunyan
description 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.
doi_str_mv 10.14778/3547305.3547318
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