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SWASH: A Flexible Communication Framework with Sliding Window-Based Cache Sharing for Scalable DGNN Training
Summary: SWASH: sliding-window cache sharing for scalable distributed DGNN training. Lightweight, sliding-window-aware partitioning with adaptive scheduling and boundary-embedding caches reduces partitioning/communication overhead while preserving accuracy, yielding 9.44x speedups over state-of-the-art.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 7275
- Venue
- SIGMOD
- Year
- 2025
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,515 | 26.93%
- DOI
-
10.1145/3725360
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Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 284 |
Querying K-Truss Community in Large and Dynamic Graphs |
2014 |
SIGMOD |
0.00029014359 |
| 635 |
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding |
2021 |
SIGMOD |
0.00018825777 |
| 1,162 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013573136 |
| 1,388 |
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs |
2022 |
VLDB |
0.00012249747 |
| 3,028 |
NeutronStar: Distributed GNN Training with Hybrid Dependency Management |
2022 |
SIGMOD |
7.6833093e-05 |
| 4,054 |
Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees |
2023 |
SIGMOD |
6.4910804e-05 |
| 5,016 |
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks |
2023 |
SIGMOD |
5.7512359e-05 |
| 5,356 |
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams |
2024 |
VLDB |
5.5514335e-05 |
| 5,530 |
Efficient Streaming Subgraph Isomorphism with Graph Neural Networks |
2021 |
VLDB |
5.4562393e-05 |
| 5,721 |
DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training |
2024 |
VLDB |
5.3538607e-05 |
| 6,008 |
Compression of Uncertain Trajectories in Road Networks |
2020 |
VLDB |
5.2365238e-05 |
| 6,479 |
EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs |
2023 |
SIGMOD |
5.0405101e-05 |
| 7,568 |
ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling |
2023 |
SIGMOD |
4.7044808e-05 |
| 9,453 |
TRACE: Real-time Compression of Streaming Trajectories in Road Networks |
2021 |
VLDB |
4.3363262e-05 |
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