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I tried to implement a graph neural network back in 2006...
https://github.com/fedd/weighscore
As expected, networkx couldn't handle more than a million nodes so I had to search for python libs which might handle that much data.
This is why I've been using your lib (https://github.com/VHRanger/nodevectors) for at least 2 weeks now as well as these 2 other libs: https://github.com/louisabraham/fastnode2vec and https://github.com/sknetwork-team/scikit-network. What do they have in common? They handle sparse graphs (using CSR representations).
Having a graph with several million nodes isn't just some edge case, social graph for instance grow way faster than anyone could expect.