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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)

Paper ID
6739
Venue
SIGMOD
Year
2023
Pagerank
5.7567672e-05
Overall Rank
5,018 | 65.10%
DOI
10.1145/3626724

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