Efficient Graph Generation with Graph Recurrent Attention Networks

Understand

We propose a new family of efficient and expressive deep generative models of graphs, called Graph Recurrent Attention Networks (GRANs).

• Our model generates graphs one block of nodes and associated edges at a time.
• The block size and sampling stride allow us to trade off sample quality for efficiency.
• Compared to previous RNN-based graph generative models, our framework better captures the auto-regressive conditioning between the already-generated and to-be-generated parts of the graph using Graph Neural Networks (GNNs) with attention.