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. This not only reduces the dependency on node ordering but also
bypasses the long-term bottleneck caused by the sequential nature of RNNs.
Moreover, we parameterize the output distribution per block using a mixture of
Bernoulli, which captures the correlations among generated edges within the
block. Finally, we propose to handle node orderings in generation by
marginalizing over a family of canonical orderings. On standard benchmarks, we
achieve state-of-the-art time efficiency and sample quality compared to
previous models. Additionally, we show our model is capable of generating large
graphs of up to 5K nodes with good quality. To the best of our knowledge, GRAN
is the first deep graph generative model that can scale to this size. Our code
is released at: this https URL

本文提出了一种名为 GRAN 的新型图形回归注意力网络的有效且表达性强的深度生成模型，能够生成高质量大规模图形，同时与之前的 RNN-based 生成模型相比，GRAN 使用了 GNN 和注意力机制更好地捕捉到已生成和待生成部分的条件之间的自回归关系，并在输出分布上采用伯努利分布的混合来捕捉区块内的生成边之间的相关性，同时在处理节点顺序方面也提出了一些新的解决方法。