GNNs need Sparse Tensors; can we do this?

[ghsanti]

Hi everyone,

Motivation

I describe very briefly what I'd like to do (related to OP.), question is at the bottom.

Here is a very well cited and simple paper (40K) for convolutions on graphs (I'd like to use it for chemistry.)

Torch Geometric

It would be easy with Torch Geometric (PyG):

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data

I want to use Keras for multi backend.

The formula in the paper seems trivial to implement in a layer:

but they talk about linear increase of complexity with the number of edges, surely this is due to sparse matrices.

Question 0: Yet, it seems Sparse Tensors support still on queue?

Question 1: We need to re-implement the graph-like layers to get multi-backend, right ? Otherwise one would just PyG.

Question 2: Can one use such sparse representations in pure Keras, chaging then the backend ? I can't find a tutorial or ref, apart from TF itself here.