[Solved] PyTorch Geometric GCN Autoencoder with Flat Latent Space

Tim Hargreaves Asks: PyTorch Geometric GCN Autoencoder with Flat Latent Space
I have a problem in which I have a series of observations, each of which is a graph of the same structure, but with different node features. I would like to learn a flat embedding of each graph of size 32×1.

My thought was to do this with an autoencoder. This would take the input graph, apply some graph convolutions, use a dense layer to map the graph to a 32×1 latent space, and then reconstruct the graph (using the same common structure) before applying a few more convolutions.

As far as I am aware, this is in contrast to the typical graph autoencoder framework, in which the latent representation is a graph of the same structure as the input but with latent representations of each nodes’ features.

For this reason, I am not sure how to implement such an architecture using PyTorch Geometric. Namely, I am unsure how I go from the flat latent space back to a graph.

Is this possible, and if so, roughly how would I do so?

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