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OUTRE: An OUT-of-core De-REdundancy GNN Training Framework for Massive Graphs within A Single Machine

Summary: Identifies neighborhood and temporal redundancies in out-of-core sampling-based GNN training and reframes the bottleneck as excessive overall data-request volume rather than cache-hit optimization. OUTRE uses partition-based batch construction, a historical-embedding cache, and automatic cache-space management to de-redundancy I/O on a single machine, yielding 1.52–3.51× speedups vs. state-of-the-art. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13703
Venue
VLDB
Year
2024
Pagerank
5.7684339e-05
Overall Rank
6,803 | 53.33%
DOI
10.14778/3681954.3681976

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{sheng_vldb24,
        title = {{OUTRE: An OUT-of-core De-REdundancy GNN Training Framework for Massive Graphs within A Single Machine}},
        author = {Sheng, Zeang and Zhang, Wentao and Tao, Yangyu and Cui, Bin},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {11},
        pages = {2960--2973},
        doi = {10.14778/3681954.3681976},
        url = {https://doi.org/10.14778/3681954.3681976},
        year = {2024}
}

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