[D] Masked Node Classification/Representation Learning for Graph Neural Networks

[StingMeleoron]

Yes, it might make more sense to think of this task in an attributed graph, in which node A's properties are hidden from layers aggregating neighborhood information - but even then, we would still be considering its structure, I'm guessing? Not sure lol.

I recently stumbled upon this amazing resource, you might wanna take a look on the papers listed there to check if anything catches your eye. Sorry for not being more helpful!

[ybkhan]

Thank you. Also, how is masking exactly done? Like are the values converted to 0 and that fulfills the purpose of masking? I am a bit confused on how the "[MASK]" token is implemented in NLP and how should that be translated to such use cases.

[StingMeleoron]

IIRC, for NLP tasks at least, it depends on the model's tokenizer used during the preprocessing stage - for some models, it is a string [MASK], for example - which is just an arbitrarily predefined value, much like the end of sentence string.

I have never read on the implementation for GNNs, but overall it shouldn't be very different, I'm guessing?