JuliaGraphs / JuliaGraphs/GraphNeuralNetworks.jl
add tutorials/examples on Equivariant GNNs
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Description
From @rbSparky comment https://github.com/CarloLucibello/GraphNeuralNetworks.jl/issues/412#issuecomment-2086942229
After further discussion(on Slack), since it has been decided that it would be more valuable to implement more relevant applications, this discussion thread can now be used for writing the tutorial for "Molecular Property Prediction using EGNNs". It can serve as a part 2 to the current published tutorial, adding a regression task(using QM9) since currently only classification is shown
Paper
Dataset to be used: QM9
The above techniques from DropMessage can be incorporated as data augmentation technique (to show the user how to write custom functions for tasks like these)
This tutorial is in progress!
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start from the current published tutorial and review the linked EGNN paper and QM9 dataset requirements. Extend it with molecular property regression and demonstrate custom DropMessage data augmentation; done means the tutorial provides the requested application and examples.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100