DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks
Summary: Chunk-based partitioning of dynamic graphs with graph coarsening to balance DGNN workloads under non-uniform spatio-temporal sparsity. Chunk fusion and adaptive stale aggregation yield 1.25x–7.52x speedups over state-of-the-art DGNN training on 3 models and 4 datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Fahao Chen
- 2. Peng Li
- 3. Celimuge Wu
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,710 | DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training | 2024 | VLDB | 5.3590055e-05 |
| 10,035 | SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine | 2026 | SIGMOD | 4.1945683e-05 |
| 10,267 | FlareDTDG: Harnessing Temporal Recency for Scalable Discrete-Time Dynamic Graph Training | 2026 | VLDB | 4.1945683e-05 |
| 10,506 | SWASH: A Flexible Communication Framework with Sliding Window-Based Cache Sharing for Scalable DGNN Training | 2025 | SIGMOD | 4.1945683e-05 |
| 10,735 | Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 278 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00029230623 |
| 1,160 | Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks | 2022 | VLDB | 0.00013586221 |
| 1,329 | AGL: A Scalable System for Industrial-purpose Graph Machine Learning | 2020 | VLDB | 0.00012561848 |
| 1,387 | TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs | 2022 | VLDB | 0.00012261568 |
| 2,677 | HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework | 2022 | VLDB | 8.3268401e-05 |
| 3,025 | NeutronStar: Distributed GNN Training with Hybrid Dependency Management | 2022 | SIGMOD | 7.6906935e-05 |
| 6,566 | Reliable Data Distillation on Graph Convolutional Network | 2020 | SIGMOD | 5.0074274e-05 |
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