Hi

I’m interested in utilizing graph VAEs in a coupled auto-encoder setting. My data is as follows: I have two graphs: F \in \mathbb{R}^{N \times N} and S \in \mathbb{R}^{N \times N}. These will be dense, fully connected graphs, with continuous edge weights. Ultimately, I want to be able to predict F from S, and vice versa.

For now, I would like to know: how can I use a VAE approach, not to learn the binary adjacency structure, but to learn the edge weights? The inner product decoder is typically used to model the probability of an edge, but not the weight of the edge itself.