Can FastGAE be used in weighted graph?
- Dominant language
- Python
- Stars
- 27
- Forks
- 6
- PR merge metrics
- No merged PRs in 30d
Description
I hope to use graph variational autoencoders in weighted biological networks with hundreds of thousands of nodes and millions of edges. FastGAE can help handle such a large network, but we noticed $A_{i,j} \in$ { 0, 1 } in Proposition 4.
Could you please tell me whether FastGAE can be used for weighted networks?

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Research direction
The issue points to Proposition 4 in the FastGAE article and provides an image, but names no repository file or test. Start by checking the implementation and derivation around the binary adjacency assumption; done means clarifying whether weighted biological networks are supported or identifying the required scope.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100