Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
NeutralArtificial Intelligence
A recent study explores whether classic Graph Neural Networks (GNNs) can serve as strong baselines for graph-level tasks, despite criticisms regarding their expressiveness and challenges like over-smoothing. The research contrasts GNNs with Graph Transformers (GTs), which utilize global attention mechanisms to address these issues. This discussion is significant as it could reshape how we view the effectiveness of GNNs in comparison to GTs, potentially influencing future research and applications in graph-based learning.
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