JuliaGraphs / JuliaGraphs/GraphNeuralNetworks.jl
Implement more pooling operators
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- Dominant language
- Julia
- Stars
- 308
- Forks
- 74
- Avg merge
- 3d 6h
- Merged PRs (30d)
- 2
Description
This is the list of pooling operators in pytorch geometric
- global_add_pool (
GlobalPool(+)here) - global_mean_pool (
GlobalPool(mean)here) - global_max_pool (
GlobalPool(max)here) - global_sort_pool
- GlobalAttention (
GlobaAttentionlPool(max)here) - Set2Set
- GraphMultisetTransformer
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 by reading the existing GlobalPool implementations for global_add_pool, global_mean_pool, and global_max_pool, then compare their interfaces with the PyTorch Geometric pooling operators linked in the issue. Implement global_sort_pool, Set2Set, and GraphMultisetTransformer consistently with those existing operators; done means all three unchecked pooling operators are available and their behavior is covered by the project's validation.
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
- Mostly clear
- Newbie friendliness
- 30/100