JuliaGraphs / JuliaGraphs/GraphNeuralNetworks.jl
GNNLux feature parity
Open
Nobody has claimed this yet.
lux
- Dominant language
- Julia
- Stars
- 308
- Forks
- 74
- Avg merge
- 3d 6h
- Merged PRs (30d)
- 2
Description
- heterograph convolutional layers
- tutorials and examples (#544)
- pooling layers
Contributor guide
No contributing guide indexed for this repository
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
The issue is a broad GNNLux feature-parity checklist covering heterograph convolutional layers, tutorials and examples in #544, and pooling layers. No files, tests, or entry points are named, so first map each item to the existing implementation and related examples; done requires completing the agreed parity scope and verifying the corresponding examples or tests.
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
- 25/100