Methods › Graphs › Graph Models › FastGCN
FastGCN
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
FastGCN is a fast improvement of the GCN model recently proposed by Kipf & Welling (2016a) for learning graph embeddings. It generalizes transductive training to an inductive manner and also addresses the memory bottleneck issue of GCN caused by recursive expansion of neighborhoods. The crucial ingredient is a sampling scheme in the reformulation of the loss and the gradient, well justified through an alternative view of graph convoluntions in the form of integral transforms of embedding functions.
Description and image from: FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
Papers archive 2025-07-28
5 shown of 5, 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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A Local Graph Limits Perspective on Sampling-Based GNNs 17 Oct 2023 · 0 repositories · arXiv:2310.10953
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On Batch-size Selection for Stochastic Training for Graph Neural Networks 1 Jan 2021 · 0 repositories
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Urban Traffic Flow Forecast Based on FastGCRNN 17 Sep 2020 · 0 repositories · arXiv:2009.08087
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Simplifying Graph Convolutional Networks 19 Feb 2019 · 7 repositories · arXiv:1902.07153Syntology ran 3 of 8 samples · 5 unverified
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FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling 30 Jan 2018 · 4 repositories · arXiv:1801.10247Syntology ran 0 of 3 samples · 3 unverified · 3 pointer-only (licence)
Tasks archive 2025-07-28
10 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
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