Methods › Graphs › Graph Models › AdaGPR
AdaGPR
Introduced by Kishan Wimalawarne et al. in Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
AdaGPR is an adaptive, layer-wise graph convolution model. AdaGPR applies adaptive generalized Pageranks at each layer of a GCNII model by learning to predict the coefficients of generalized Pageranks using sparse solvers.
Papers archive 2025-07-28
1 shown of 1, 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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Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank 24 Aug 2021 · 0 repositories · arXiv:2108.10636
Tasks archive 2025-07-28
2 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 |
|---|---|
| Generalization Bounds | 1 |
| Node Classification | 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
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