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I have a use-case to train graph embeddings, looking for a way to do it in pytorch and tensorflow. The restriction is the methodology should incorporate edge weights in calculating transition probability. Can someone please advise the best framework/library to do. The node2vec implementation by the authors is in spark only, however my current ML pipeline is more pytorch/TF friendly.

https://github.com/aditya-grover/node2vec

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Don't know if this is what you need but I know of the Ampligraph library:

Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org

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