FlareDTDG: Harnessing Temporal Recency for Scalable Discrete-Time Dynamic Graph Training

Summary: FlareDTDG: distributed DTDG training that exploits temporal recency via hybrid batching with temporal decay—full-batch on recent snapshots, coarse sampling on older ones. Adds shrinking-based graph reconstruction and adaptive comm/comp overlap for 1.4–2.5x speedups, 10–85% less GPU memory, near-lossless accuracy. (summarized by gpt-5.4-mini on May 27 2026)

Paper ID: 14304
Venue: VLDB
Year: 2026
Pagerank: 4.1945683e-05
Overall Rank: 10,267 | 28.58%
DOI: 10.14778/3801059.3801073

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,160	Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks	2022	VLDB	0.00013586221
5,018	DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks	2023	SIGMOD	5.7567672e-05
5,710	DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training	2024	VLDB	5.3590055e-05
10,656	Effective and Efficient Distributed Temporal Graph Learning through Hotspot Memory Sharing	2025	VLDB	4.1945683e-05

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