Papers › Beyond Homophily with Graph Echo State Networks
Beyond Homophily with Graph Echo State Networks
Domenico Tortorella, Alessio Micheli
Graph Echo State Networks (GESN) have already demonstrated their efficacy and efficiency in graph classification tasks. However, semi-supervised node classification brought out the problem of over-smoothing in end-to-end trained deep models, which causes a bias towards high homophily graphs. We evaluate for the first time GESN on node classification tasks with different degrees of homophily, analyzing also the impact of the reservoir radius. Our experiments show that reservoir models are able to achieve better or comparable accuracy with respect to fully trained deep models that implement ad hoc variations in the architectural bias, with a gain in terms of efficiency.
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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 | Graph ESN | Accuracy | 34.5 ± 0.8 | #51 of 62 | Archive leaderboard | report |
| Node Classification | Chameleon | Graph ESN | Accuracy | 76.2±1.2 | #8 of 61 | Archive leaderboard | report |
| Node Classification | Citeseer Full-supervised | Graph ESN | Accuracy | 74.5±2.1 | #6 of 7 | Archive leaderboard | report |
| Node Classification | Cora Full-supervised | Graph ESN | Accuracy | 86.0±1.0 | #5 of 9 | Archive leaderboard | report |
| Node Classification | Cornell | Graph ESN | Accuracy | 81.1±6.0 | #39 of 60 | Archive leaderboard | report |
| Node Classification | Pubmed Full-supervised | Graph ESN | Accuracy | 89.2±0.3 | #5 of 7 | Archive leaderboard | report |
| Node Classification | Squirrel | Graph ESN | Accuracy | 71.2±1.5 | #8 of 59 | Archive leaderboard | report |
| Node Classification | Texas | Graph ESN | Accuracy | 84.3±4.4 | #37 of 62 | Archive leaderboard | report |
| Node Classification | Wisconsin | Graph ESN | Accuracy | 83.3±3.8 | #46 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.
Methods
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