Closed emsal0 closed 2 years ago
The bypass_graph
is designed to pass node/edge/global features over distinct networks. You want to pass each node features over a neural network. I am curious what is the difference between passing node features one by one and passing whole node features as a matrix? Computationally, it should be the same and we don't need to separate node features for that.
In the latest version, bypass_graph
has been deprecated. The functionality is replaced by GraphParallel
layer.
https://github.com/FluxML/GeometricFlux.jl/blob/c8396e4a34637c06c5c479851877c44e1a85a530/src/layers/misc.jl#L6-L13
Say I want to use a
Flux.Chain
to apply a neural network to each node feature vector. I don't think I can just pass in theChain
here, because it will be applied to the whole node feature matrix, which a) doesn't separate the node features and b) might be of variable size. Should the functionality ofbypass_graph
be changed so that it applies a mapping over node feature vectors instead?