Methods › Graphs › Graph Representation Learning › GraphSAINT
Graph sampling based inductive learning method
GraphSAINT
Introduced by Hanqing Zeng et al. in GraphSAINT: Graph Sampling Based Inductive Learning Method
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Scalable method to train large scale GNN models via sampling small subgraphs.
Papers archive 2025-07-28
7 shown of 7, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Lifelong Graph Learning for Graph Summarization 25 Jul 2024 · 1 repository · arXiv:2407.18042
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PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks 25 Jun 2023 · 0 repositories · arXiv:2306.14357
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BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs 4 May 2023 · 0 repositories · arXiv:2305.02522
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A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs 11 Nov 2022 · 1 repository · arXiv:2211.06292Syntology ran 0 of 3 samples · 3 unverified
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Decoupling the Depth and Scope of Graph Neural Networks 19 Jan 2022 · 1 repository · arXiv:2201.07858Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
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On Batch-size Selection for Stochastic Training for Graph Neural Networks 1 Jan 2021 · 0 repositories
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GraphSAINT: Graph Sampling Based Inductive Learning Method 10 Jul 2019 · 8 repositories · arXiv:1907.04931
Tasks archive 2025-07-28
15 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections