Hi!
I’m working on an edge classification task, Ive already seen the documentation example, but im still not sure, what is the correct way to split a dataset that consists of a single graph for edge classification? I want to use neighbor sampling during training. How can I split the graph to separate the edges into training, validation, and test sets while applying neighbor sampling specifically during training?
you need to split the dataset(namely edge ID or node pairs) into train/val/test on your own before instantiating dataloader.
What I ended up doing was creating a train mask and a test mask for the edata, then extracting their IDs and passing them to the dataloader with neighbor sampling.
Thank you for helping me understand!
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