Papers › Understanding over-squashing and bottlenecks on graphs via curvature
Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein
Most graph neural networks (GNNs) use the message passing paradigm, in which node features are propagated on the input graph. Recent works pointed to the distortion of information flowing from distant nodes as a factor limiting the efficiency of message passing for tasks relying on long-distance interactions. This phenomenon, referred to as 'over-squashing', has been heuristically attributed to graph bottlenecks where the number of k-hop neighbors grows rapidly with k. We provide a precise description of the over-squashing phenomenon in GNNs and analyze how it arises from bottlenecks in the graph. For this purpose, we introduce a new edge-based combinatorial curvature and prove that negatively curved edges are responsible for the over-squashing issue. We also propose and experimentally test a curvature-based graph rewiring method to alleviate the over-squashing.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Classification | Actor | SDRF | Accuracy | 28.42 ± 0.75 | #62 of 62 | Archive leaderboard | report |
| Node Classification | Chameleon | SDRF | Accuracy | 42.73±0.15 | #61 of 61 | Archive leaderboard | report |
| Node Classification | Citeseer | SDRF | Accuracy | 72.58±0.20 | #41 of 71 | Archive leaderboard | report |
| Node Classification | Cora | SDRF | Accuracy | 82.76±0.23% | #49 of 73 | Archive leaderboard | report |
| Node Classification | Cornell | SDRF | Accuracy | 54.60±0.39 | #59 of 60 | Archive leaderboard | report |
| Node Classification | Pubmed | SDRF | Accuracy | 79.10±0.11 | #52 of 70 | Archive leaderboard | report |
| Node Classification | Squirrel | SDRF | Accuracy | 37.05±0.17 | #53 of 59 | Archive leaderboard | report |
| Node Classification | Texas | SDRF | Accuracy | 64.46±0.38 | #58 of 62 | Archive leaderboard | report |
| Node Classification | Wisconsin | SDRF | Accuracy | 55.51±0.27 | #63 of 63 | 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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