Methods › Graphs › Graph Models › LGCL
Learnable graph convolutional layer
LGCL
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Learnable graph convolutional layer (LGCL) automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs.
Description and image from: Large-Scale Learnable Graph Convolutional Networks
Papers archive 2025-07-28
4 shown of 4, 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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Introducing Language Guidance in Prompt-based Continual Learning 30 Aug 2023 · 1 repository · arXiv:2308.15827Syntology ran 1 of 1 samples · 0 unverified
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Line Graph Contrastive Learning for Link Prediction 25 Oct 2022 · 1 repository · arXiv:2210.13795
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3D Shapes Local Geometry Codes Learning with SDF 19 Aug 2021 · 0 repositories · arXiv:2108.08593
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Large-Scale Learnable Graph Convolutional Networks 12 Aug 2018 · 1 repository · arXiv:1808.03965
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
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