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I recently came across the paper A Survey on Oversmoothing in Graph Neural Networks and thougt that having a ready-to-use implementation of the Dirichlet energy and the Mean Average Distance would make diagnosing oversmoothing much easier.
Alternatives
I saw that PairNorm has been implemented but I think it'd be nice to be able to quantitatively examine the behavior of node similarity (at least for academic & research purposes).
Additional context
If this is something people would find useful if it was included in pytorch_geometric I could work on this.
Edit: Corrected paper link
The text was updated successfully, but these errors were encountered:
I now have a preliminary implementation of both the Dirichlet Energy and the Mean Average Distance. The code can be found here in my node_similarity_pyg repository (I plan to add a notebook on how to replicate some of the empirical results of the paper in a few days).
I don't really know where in pytorch-geometric these methods would fit best, maybe in torch_geometric.utils?
馃殌 The feature, motivation and pitch
I recently came across the paper A Survey on Oversmoothing in Graph Neural Networks and thougt that having a ready-to-use implementation of the Dirichlet energy and the Mean Average Distance would make diagnosing oversmoothing much easier.
Alternatives
I saw that
PairNorm
has been implemented but I think it'd be nice to be able to quantitatively examine the behavior of node similarity (at least for academic & research purposes).Additional context
If this is something people would find useful if it was included in
pytorch_geometric
I could work on this.Edit: Corrected paper link
The text was updated successfully, but these errors were encountered: