Methods › Graphs › Graph Models › MinCutPool
MinCut Pooling
MinCutPool
Introduced by Filippo Maria Bianchi et al. in Spectral Clustering with Graph Neural Networks for Graph Pooling
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
MinCutPool is a trainable pooling operator for graphs that learns to map nodes into clusters. The method is trained to approximate the minimum K-cut of the graph to ensure that the clusters are balanced, while also jointly optimizing the objective of the task at hand.
Papers archive 2025-07-28
2 shown of 2, 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.
-
Tackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach 16 Jul 2024 · 0 repositories · arXiv:2407.11928
-
Spectral Clustering with Graph Neural Networks for Graph Pooling 30 Jun 2019 · 4 repositories · arXiv:1907.00481Syntology ran 0 of 9 samples · 9 unverified
Tasks archive 2025-07-28
5 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 |
|---|---|
| Graph Classification | 2 |
| Graph Neural Network | 2 |
| Clustering | 1 |
| Graph Clustering | 1 |
| Semantic Segmentation | 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