Summary of Classic Gnns Are Strong Baselines: Reassessing Gnns For Node Classification, by Yuankai Luo et al.
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
by Yuankai Luo, Lei Shi, Xiao-Ming Wu
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper reevaluates the performance of three classic Graph Neural Networks (GNNs) against Graph Transformers (GTs) on 18 diverse datasets. The authors find that previously reported GT superiority may be overstated due to suboptimal hyperparameters, and that slight tuning can achieve state-of-the-art GNN performance. The study also investigates the influence of various GNN configurations on node classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper compares how well Graph Neural Networks (GNNs) and Graph Transformers (GTs) work at identifying what kind of nodes are in a graph. It shows that GTs might not be as good as people thought they were because the settings used for GNNs weren’t perfect. If you tweak the GNN settings just right, it can actually do better than GTs! The study also looks into how different settings affect how well GNNs work. |
Keywords
» Artificial intelligence » Classification » Gnn
