Papers › A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks
A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks
Davide Bacciu, Luigi Di Sotto
The paper discusses a pooling mechanism to induce subsampling in graph structured data and introduces it as a component of a graph convolutional neural network. The pooling mechanism builds on the Non-Negative Matrix Factorization (NMF) of a matrix representing node adjacency and node similarity as adaptively obtained through the vertices embedding learned by the model. Such mechanism is applied to obtain an incrementally coarser graph where nodes are adaptively pooled into communities based on the outcomes of the non-negative factorization. The empirical analysis on graph classification benchmarks shows how such coarsening process yields significant improvements in the predictive performance of the model with respect to its non-pooled counterpart.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Classification | COLLAB | 1-NMFPool | Accuracy | 65.0% | #38 of 39 | Archive leaderboard | report |
| Graph Classification | D&D | 1-NMFPool | Accuracy | 76.0% | #41 of 53 | Archive leaderboard | report |
| Graph Classification | ENZYMES | 1-NMFPool | Accuracy | 24.1% | #53 of 54 | Archive leaderboard | report |
| Graph Classification | NCI1 | 1-NMFPool | Accuracy | 66.2% | #67 of 69 | Archive leaderboard | report |
| Graph Classification | PROTEINS | 1-NMFPool | Accuracy | 72.1% | #95 of 103 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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