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[D] What are some graph data augmentations?
For those of you who do Graph Deep Learning or inference on graphs, what are some good data augmentations you use on your graph data? So far, there's only one paper (https://arxiv.org/pdf/2006.06830.pdf) that documents something like this and it mentions stuff like dropping nodes, dropping edges, perturbing features, etc. What are some others you've come across / practice in your work?
And do your libraries (like GDL, PyG, etc.) come with such augmentations in the form of …!-->