Papers › Half-Hop: A graph upsampling approach for slowing down message passing
Half-Hop: A graph upsampling approach for slowing down message passing
Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Veličković, Eva L. Dyer
Message passing neural networks have shown a lot of success on graph-structured data. However, there are many instances where message passing can lead to over-smoothing or fail when neighboring nodes belong to different classes. In this work, we introduce a simple yet general framework for improving learning in message passing neural networks. Our approach essentially upsamples edges in the original graph by adding "slow nodes" at each edge that can mediate communication between a source and a target node. Our method only modifies the input graph, making it plug-and-play and easy to use with existing models. To understand the benefits of slowing down message passing, we provide theoretical and empirical analyses. We report results on several supervised and self-supervised benchmarks, and show improvements across the board, notably in heterophilic conditions where adjacent nodes are more likely to have different labels. Finally, we show how our approach can be used to generate augmentations for self-supervised learning, where slow nodes are randomly introduced into different edges in the graph to generate multi-scale views with variable path lengths.
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0ca29413fb690b70 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Classification | AMZ Comp | HH-GCN | Accuracy | 90.92% | #1 of 7 | Archive leaderboard | report |
| Node Classification | AMZ Comp | GCN | Accuracy | 90.22% | #2 of 7 | Archive leaderboard | report |
| Node Classification | AMZ Comp | HH-GraphSAGE | Accuracy | 86.6% | #4 of 7 | Archive leaderboard | report |
| Node Classification | AMZ Comp | GraphSAGE | Accuracy | 84.79% | #6 of 7 | Archive leaderboard | report |
| Node Classification | AMZ Photo | GraphSAGE | Accuracy | 95.03% | #5 of 14 | Archive leaderboard | report |
| Node Classification | AMZ Photo | HH-GraphSAGE | Accuracy | 94.55% | #6 of 14 | Archive leaderboard | report |
| Node Classification | AMZ Photo | HH-GCN | Accuracy | 94.52% | #7 of 14 | Archive leaderboard | report |
| Node Classification | AMZ Photo | GCN | Accuracy | 93.59% | #9 of 14 | Archive leaderboard | report |
| Node Classification | Chameleon (60%/20%/20% random splits) | HH-GraphSAGE | 1:1 Accuracy | 62.98 ± 3.35 | #23 of 38 | Archive leaderboard | report |
| Node Classification | Chameleon (60%/20%/20% random splits) | HH-GAT | 1:1 Accuracy | 61.12 ± 1.83 | #28 of 38 | Archive leaderboard | report |
| Node Classification | Chameleon (60%/20%/20% random splits) | HH-GCN | 1:1 Accuracy | 60.24 ± 1.93 | #32 of 38 | Archive leaderboard | report |
| Node Classification | Coauthor CS | HH-GraphSAGE | Accuracy | 95.13% | #12 of 24 | Archive leaderboard | report |
| Node Classification | Coauthor CS | GraphSAGE | Accuracy | 95.11% | #13 of 24 | Archive leaderboard | report |
| Node Classification | Coauthor CS | HH-GCN | Accuracy | 94.71% | #16 of 24 | Archive leaderboard | report |
| Node Classification | Coauthor CS | GCN | Accuracy | 94.06% | #17 of 24 | Archive leaderboard | report |
| Node Classification | Cornell (60%/20%/20% random splits) | HH-GraphSAGE | 1:1 Accuracy | 74.6 ± 6.06 | #27 of 36 | Archive leaderboard | report |
| Node Classification | Cornell (60%/20%/20% random splits) | HH-GAT | 1:1 Accuracy | 72.7 ± 4.26 | #29 of 36 | Archive leaderboard | report |
| Node Classification | Cornell (60%/20%/20% random splits) | HH-GCN | 1:1 Accuracy | 63.24 ± 5.43 | #34 of 36 | Archive leaderboard | report |
| Node Classification | Squirrel (60%/20%/20% random splits) | HH-GCN | 1:1 Accuracy | 47.19 ± 1.21 | #19 of 37 | Archive leaderboard | report |
| Node Classification | Squirrel (60%/20%/20% random splits) | HH-GAT | 1:1 Accuracy | 46.35 ± 1.86 | #21 of 37 | Archive leaderboard | report |
| Node Classification | Squirrel (60%/20%/20% random splits) | HH-GraphSAGE | 1:1 Accuracy | 45.25 ± 1.52 | #22 of 37 | Archive leaderboard | report |
| Node Classification | Texas (60%/20%/20% random splits) | HH-GraphSAGE | 1:1 Accuracy | 85.95 ± 6.42 | #21 of 36 | Archive leaderboard | report |
| Node Classification | Texas (60%/20%/20% random splits) | HH-GAT | 1:1 Accuracy | 80.54 ± 4.80 | #30 of 36 | Archive leaderboard | report |
| Node Classification | Texas (60%/20%/20% random splits) | HH-GCN | 1:1 Accuracy | 71.89 ± 3.46 | #35 of 36 | Archive leaderboard | report |
| Node Classification | Wiki-CS | GraphSAGE | Accuracy | 83.67 | #2 of 6 | Archive leaderboard | report |
| Node Classification | Wiki-CS | HH-GraphSAGE | Accuracy | 82.81 | #3 of 6 | Archive leaderboard | report |
| Node Classification | Wiki-CS | HH-GCN | Accuracy | 82.57 | #4 of 6 | Archive leaderboard | report |
| Node Classification | Wiki-CS | GCN | Accuracy | 81.93 | #5 of 6 | Archive leaderboard | report |
| Node Classification | Wisconsin (60%/20%/20% random splits) | HH-GraphSAGE | 1:1 Accuracy | 85.88 ± 3.99 | #21 of 35 | Archive leaderboard | report |
| Node Classification | Wisconsin (60%/20%/20% random splits) | HH-GAT | 1:1 Accuracy | 83.53 ± 3.84 | #22 of 35 | Archive leaderboard | report |
| Node Classification | Wisconsin (60%/20%/20% random splits) | HH-GCN | 1:1 Accuracy | 79.8 ± 4.30 | #24 of 35 | 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.
Methods
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