JuliaGraphs / JuliaGraphs/GraphNeuralNetworks.jl
Spatial Graph Convolutional Networks
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Description
Hi, many thanks for your work on this amazing package.
There is a paper called "Spatial Graph Convolutional Networks", with accompanying PyTorch code. You may be familiar with this work already but, from my understanding, the premise of this architecture is that it is a proper generalisation of CNNs from gridded spatial data to irregular spatial data; see Figure 3 and Theorem 1 in the paper.
Do you think it would be worthwhile to include this architecture in your package, perhaps as one of the convolutional layers?
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
Read the linked paper, especially Figure 3 and Theorem 1, then compare its architecture with the convolutional layers listed in the package API. Review the linked PyTorch implementation and determine the expected scope and acceptance criteria before attempting work; the issue does not currently define what completion would look like.
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