Methods › Graphs › Graph Representation Learning › L-GCN
Learnable adjacency matrix GCN
L-GCN
Introduced by Salva Rühling Cachay et al. in The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Graph structure is learnable
Papers archive 2025-07-28
3 shown of 3, 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.
-
Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation 6 Nov 2023 · 0 repositories · arXiv:2311.02938
-
ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models 29 Nov 2021 · 1 repository · arXiv:2111.14671
-
The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks 11 Apr 2021 · 2 repositories · arXiv:2104.05089
Tasks archive 2025-07-28
6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Benchmarking | 1 |
| Contrastive Learning | 1 |
| Deep Learning | 1 |
| Physical Simulations | 1 |
| Relation | 1 |
| Session-Based Recommendations | 1 |
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