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Truss Decomposition in Hypergraphs

Summary: Introduces hypertruss decomposition, where each node participates in at least k hyper-triangles. Its key contribution is scalable hyper-triangle counting via hybrid edge/node iterators and prefix forests, with ordering optimizations for shared-node hypergraphs. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
hba4bb6f976f8e496
Venue
VLDB
Year
2025
Pagerank
5.3350162e-05
Overall Rank
8,419 | 43.40%
DOI
10.14778/3734839.3734854

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{qin_vldb25,
        title = {{Truss Decomposition in Hypergraphs}},
        author = {Qin, Hongchao and Zeng, Guang and Li, Rong-Hua and Lin, Longlong and Yuan, Ye and Wang, Guoren},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {7},
        pages = {2185--2197},
        doi = {10.14778/3734839.3734854},
        url = {https://doi.org/10.14778/3734839.3734854},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
10,806 Efficient Hyper-truss Decomposition over Hypergraphs 2026 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 16 of 16 cited papers.

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

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