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We propose a combination of a variational autoencoder and a transformer basedmodel which fully utilises graph convolutional and graph pooling layers tooperate directly on graphs. The transformer model implements a novel nodeencoding layer, replacing the position encoding typically used in transformers,to create a transformer with no position information that operates on graphs,encoding adjacent node properties into the edge generation process. Theproposed model builds on graph generative work operating on graphs with edgefeatures, creating a model that offers improved scalability with the number ofnodes in a graph. In addition, our model is capable of learning a disentangled,interpretable latent space that represents graph properties through a mappingbetween latent variables and graph properties. In experiments we chose abenchmark task of molecular generation, given the importance of both generatednode and edge features. Using the QM9 dataset we demonstrate that our modelperforms strongly across the task of generating valid, unique and novelmolecules. Finally, we demonstrate that the model is interpretable bygenerating molecules controlled by molecular properties, and we then analyseand visualise the learned latent representation.

A Graph VAE and Graph Transformer Approach to Generating Molecular Graphs