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ABC: Attributed Bipartite Co-clustering

Summary: ABC unifies bipartite modularity optimization with attribute cohesiveness for co-clustering in attributed bipartite networks. NP-hard and not APX (unless P=NP); top-down, bottom-up, and group-matching algorithms deliver practical efficiency on real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12898
Venue
VLDB
Year
2022
Pagerank
5.5580534e-05
Overall Rank
7,727 | 46.99%
DOI
10.14778/3547305.3547318

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{kim_vldb22,
        title = {{ABC: Attributed Bipartite Co-clustering}},
        author = {Kim, Junghoon and Feng, Kaiyu and Cong, Gao and Zhu, Diwen and Yu, Wenyuan and Miao, Chunyan},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {10},
        pages = {2134--2147},
        doi = {10.14778/3547305.3547318},
        url = {https://doi.org/10.14778/3547305.3547318},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
5,877 Neural Attributed Community Search at Billion Scale 2023 SIGMOD 6.0551011e-05
10,349 Budgeted Strong Community Search in Heterogeneous Graphs 2026 SIGMOD 5.093636e-05
11,267 Efficient Maximal Frequent Group Enumeration in Temporal Bipartite Graphs 2024 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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