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DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training

Summary: DynaHB targets scalable distributed DGNN training with vertex caching and load-aware partitioning to avoid communication and imbalance. Its hybrid vertex/snapshot batches, RL-based adjustment, and pipelined reservoir cut GPU memory, batch-generation cost, and training time by up to 93×. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13738
Venue
VLDB
Year
2024
Pagerank
6.0961929e-05
Overall Rank
5,760 | 60.49%
DOI
10.14778/3681954.3682008

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{song_vldb24,
        title = {{DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training}},
        author = {Song, Zhen and Gu, Yu and Sun, Qing and Li, Tianyi and Zhang, Yanfeng and Li, Yushuai and Jensen, Christian S. and Yu, Ge},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {11},
        pages = {3388--3401},
        doi = {10.14778/3681954.3682008},
        url = {https://doi.org/10.14778/3681954.3682008},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank Citing Paper Year Venue Pagerank
10,241 FastGNAS: Accelerating and Scaling Graph Neural Architecture Search on Multi-GPUs via Ring-Based Model Migration 2026 SIGMOD 5.093636e-05
10,330 SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine 2026 SIGMOD 5.093636e-05
10,555 FlareDTDG: Harnessing Temporal Recency for Scalable Discrete-Time Dynamic Graph Training 2026 VLDB 5.093636e-05
10,620 Understanding Evolving Graph Structures for Large Discrete-Time Dynamic Graph Representation 2026 VLDB 5.093636e-05
10,780 SWASH: A Flexible Communication Framework with Sliding Window-Based Cache Sharing for Scalable DGNN Training 2025 SIGMOD 5.093636e-05
10,975 Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation 2025 VLDB 5.093636e-05
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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
223 AliGraph: A Comprehensive Graph Neural Network Platform 2019 VLDB 0.00024182473
920 APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding 2021 SIGMOD 0.00013209734
1,132 SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks 2022 VLDB 0.00012041292
2,640 Scalable and Efficient Full-Graph GNN Training for Large Graphs 2023 SIGMOD 8.3074486e-05
2,695 NeutronStar: Distributed GNN Training with Hybrid Dependency Management 2022 SIGMOD 8.2468134e-05
2,953 Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching 2022 VLDB 7.9237794e-05
3,210 Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank 2023 VLDB 7.6352864e-05
3,634 Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees 2023 SIGMOD 7.2358691e-05
4,878 NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams 2024 VLDB 6.4684388e-05
4,891 DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks 2023 SIGMOD 6.4581865e-05
6,002 Compression of Uncertain Trajectories in Road Networks 2020 VLDB 6.0133202e-05
6,261 EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs 2023 SIGMOD 5.9366952e-05
7,374 ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling 2023 SIGMOD 5.6306367e-05
7,813 TRACE: Real-time Compression of Streaming Trajectories in Road Networks 2021 VLDB 5.5390003e-05
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