deezer / deezer/fastgae

Can FastGAE be used in weighted graph?

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Dominant language
Python
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Forks
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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?

![image](https://github.com/deezer/fastgae/assets/32414349/9fc968ed-0f8c-4bf2-9247-9864f55d328f)

Contributor guide

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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.

Written by the indexing model from the issue text.

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

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