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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)

Paper ID
14133
Venue
VLDB
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,907 | 25.17%
DOI
10.14778/3746405.3746430

Incoming Non-self Citations Over Time

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Authors

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}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

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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