Efficient Hyper-truss Decomposition over Hypergraphs
Summary: Introduces a vertex-aware hyper-truss decomposition framework that avoids open-hyper-triangle traversal and reduces support enumeration via vertex-oriented counting and state propagation. Experiments on 11 real datasets show substantial efficiency gains. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Haozhe Yin (University of New South Wales)
- 2. Kai Wang (Shanghai Jiao Tong University)
- 3. Wenjie Zhang (University of New South Wales)
- 4. Xuemin Lin (Chinese University of Hong Kong)
BibTeX Citation
@article{yin_vldb26,
title = {{Efficient Hyper-truss Decomposition over Hypergraphs}},
author = {Yin, Haozhe and Wang, Kai and Zhang, Wenjie and Lin, Xuemin},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {10},
pages = {2549--2562},
doi = {10.14778/3828612.3828614},
url = {https://doi.org/10.14778/3828612.3828614},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 184 | Querying K-Truss Community in Large and Dynamic Graphs | 2014 | SIGMOD | 0.00026100147 |
| 3,314 | Accelerating Triangle Counting on GPU | 2021 | SIGMOD | 7.4380767e-05 |
| 3,539 | Hypergraph Motifs: Concepts, Algorithms, and Discoveries | 2020 | VLDB | 7.2173698e-05 |
| 4,314 | Neighborhood-based Hypergraph Core Decomposition | 2023 | VLDB | 6.6695257e-05 |
| 8,419 | Truss Decomposition in Hypergraphs | 2025 | VLDB | 5.3350162e-05 |
| 11,446 | Efficient Computation of Hyper-triangles on Hypergraphs | 2025 | VLDB | 4.9793485e-05 |
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