GPU-Accelerated eta-threshold Decomposition for Uncertain Graphs
Summary: Gatd is the first GPU framework for eta-threshold decomposition in uncertain graphs, replacing costly CPU peeling with GPU-co-designed parallelism. Lower-bound pruning, adaptive collaboration, and hierarchical load balancing deliver up to 10,000× speedups. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Yu Chen (Zhejiang University)
- 2. Chong Liu (Zhejiang University)
- 3. Qing Liu (Zhejiang University)
- 4. Zhonggen Li (Zhejiang University)
- 5. Yifan Zhu (Zhejiang University)
- 6. Yunjun Gao (Zhejiang University)
BibTeX Citation
@article{chen_vldb26,
title = {{GPU-Accelerated eta-threshold Decomposition for Uncertain Graphs}},
author = {Chen, Yu and Liu, Chong and Liu, Qing and Li, Zhonggen and Zhu, Yifan and Gao, Yunjun},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {10},
pages = {2591--2603},
doi = {10.14778/3828612.3828617},
url = {https://doi.org/10.14778/3828612.3828617},
year = {2026}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,086 | Truss Decomposition of Probabilistic Graphs: Semantics and Algorithms | 2016 | SIGMOD | 9.0689711e-05 |
| 3,653 | Realtime Top-k Personalized PageRank over Large Graphs on GPUs | 2020 | VLDB | 7.1288161e-05 |
| 3,875 | Cohesive Subgraph Search over Big Heterogeneous Information Networks: Applications, Challenges, and Solutions | 2021 | SIGMOD | 6.9536357e-05 |
| 4,732 | Fast Maximal Clique Enumeration on Uncertain Graphs: A Pivot-based Approach | 2022 | SIGMOD | 6.4461271e-05 |
| 6,249 | Self-adaptive Graph Traversal on GPUs | 2021 | SIGMOD | 5.8358127e-05 |
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