Description:
In Section 13.3.3 of the Deep Learning book ([link](https://issuu.com/cmb321/docs/deep_learning_ebook/438)) and in the corresponding paper [arXiv:1806.01261](https://arxiv.org/pdf/1806.01261), the authors incorporate both graph-level and edge-level embeddings directly into the message-passing layers, updating them at each layer. This differs from the current PyG implementations of GAT and ResGated, which use edge embeddings only to enrich node representations without updating them across layers.
By contrast, in our current approach, RDKit2DNormalized molecular features are employed only in the final linear layers for non-augmented version. It may therefore be worthwhile to investigate whether integrating and updating global graph-level features (e.g., RDKit2DNormalized) and edge features directly within the message-passing phase could improve model expressiveness and performance.
Description:
In Section 13.3.3 of the Deep Learning book ([link](https://issuu.com/cmb321/docs/deep_learning_ebook/438)) and in the corresponding paper [arXiv:1806.01261](https://arxiv.org/pdf/1806.01261), the authors incorporate both graph-level and edge-level embeddings directly into the message-passing layers, updating them at each layer. This differs from the current PyG implementations of GAT and ResGated, which use edge embeddings only to enrich node representations without updating them across layers.
By contrast, in our current approach,
RDKit2DNormalizedmolecular features are employed only in the final linear layers for non-augmented version. It may therefore be worthwhile to investigate whether integrating and updating global graph-level features (e.g.,RDKit2DNormalized) and edge features directly within the message-passing phase could improve model expressiveness and performance.