JuliaGraphs / JuliaGraphs/GraphNeuralNetworks.jl
implementation of the DropMessage paper
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 308
- Forks
- 74
- Avg merge
- 3d 6h
- Merged PRs (30d)
- 2
Description
Recently I had planned to work on some experiments related to the DropMessage paper and its extensions and application on various datasets like
- Cora
- CiteSeer
- Pubmed
- Flickr
- ogbn-arxiv
If it would be considered useful, I could write up a tutorial/blog related to the work to show GNN.jl users how to modify architectures and use them on various datasets and experiment along the way.
Experiments include:
- Edge perturbation
- Feature noise injection
- Subgraph sampling
- Diffusion
Let me know if this will be useful for the repository! 😄
Thanks
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
No repository files, tests, or entry points are identified. Start by reading the DropMessage paper and reviewing GNN.jl's existing architecture and dataset APIs; the scope and definition of done would need to be agreed before implementing experiments or a tutorial.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100