Can GNNs be used as encoders in parametric UMAP ?
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Description
Having gone through the documentation of parametric UMAP, I thought we could use graph neural networks to get the embedding of the KNN graph in a lower dimension. Is it possible to access the KNN graph(the one created in the first step of UMAP) in a higher dimension and then use GNN to update each node's embedding so as to capture neighborhood information and then reduce that updated embedding to a lower dimension using a fully connected layer?
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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 with the parametric UMAP documentation and the first-step KNN graph described in the issue. Determine whether that graph is accessible for a GNN encoder and define a concrete implementation scope and acceptance criteria before proposing work.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100