Effective and Efficient Distributed Temporal Graph Learning through Hotspot Memory Sharing
Summary: MemShare enables distributed memory-based temporal GNN training via a small cross-machine/GPU shared-node memory, reducing remote-memory communication. Shared-node-centric partitioning, boundary-decay sampling, and targeted synchronous smoothing address imbalance, staleness, and accuracy loss. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Longjiao Zhang (Zhejiang University)
- 2. Rui Wang (Institute of Blockchain and Data Security; Zhejiang University)
- 3. Tongya Zheng (Hangzhou City University)
- 4. Ziqi Huang (Zhejiang University)
- 5. Wenjie Huang (Zhejiang University)
- 6. Xinyu Wang (Zhejiang University)
- 7. Can Wang (Zhejiang University)
- 8. Mingli Song (Zhejiang University)
- 9. Sai Wu (Zhejiang University)
- 10. Shuibing He (Zhejiang University)
BibTeX Citation
@article{zhang_vldb25,
title = {{Effective and Efficient Distributed Temporal Graph Learning through Hotspot Memory Sharing}},
author = {Zhang, Longjiao and Wang, Rui and Zheng, Tongya and Huang, Ziqi and Huang, Wenjie and Wang, Xinyu and Wang, Can and Song, Mingli and Wu, Sai and He, Shuibing},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {9},
pages = {3093--3105},
doi = {10.14778/3746405.3746430},
url = {https://doi.org/10.14778/3746405.3746430},
year = {2025}
}
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,555 | FlareDTDG: Harnessing Temporal Recency for Scalable Discrete-Time Dynamic Graph Training | 2026 | VLDB | 5.093636e-05 |
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