I’m not deeply familiar with graph networks, but an accuracy of 0% sounds wrong.
In the worst case, your model should at least yield the random accuracy. E.g. for a multi-class classification use case with 10 classes, you should at least get 10% accuracy.
Could you try to create a tiny training dataset and overfit it with your model?