Graph Generation with Recurrent and Graph Neural Networks

Graph generation has applications as diverse as drug discovery, materials design, and code completion. In this paper, we propose a novel auto-regressive graph generation model, where graph generation is viewed as a decision process. The proposed model combines the power of graph neural networks (GNNs) with generative modeling techniques, and incorporates both graph topology and node features, allowing the generation of graphs with desired properties. Extensive experiments on molecule datasets demonstrate the effectiveness of our approach, achieving high validity, diversity, and similarity to the target molecules.