graphs-and-networks.
4 of 378 NOTE documents. Back to the full index.
| # | Title | Summary | Status |
|---|---|---|---|
| NOTE-172 | SuperGlue: Learning Feature Matching with Graph Neural Networks | Learn the matcher instead of hand-designing it: a GNN predicts assignment costs, differentiable optimal transport solves them, and correspondence and rejection happen jointly. Filed for the line rather than for a practice — its surviving assumption, that a detector ran first, is what the next paper identifies as the binding constraint. | Read |
| NOTE-317 | Where did the gap go? | Retuned message-passing baselines close the reported graph-transformer gap on LRGB’s Peptides datasets, mostly by adding an MLP prediction head. Feature normalization lifts every model on the superpixel datasets. Read in full. | Read |
| NOTE-318 | Pitfalls of GNN evaluation | With one shared training and tuning protocol over 100 splits × 20 seeds, GCN ranks first among four GNNs, and the single Planetoid split reorders models. Read in full. | Read |
| NOTE-319 | GIN | Message passing is bounded by 1-WL, and sum aggregation with an MLP reaches the bound where mean and max do not. The theory predicts the training-fit ordering. Test accuracy separates the aggregators decisively only on featureless graphs. Main text read, proofs skimmed. | Read |