Efficient Graph Generation with Graph Recurrent Attention Networks

NeurIPS 2019 Renjie LiaoYujia LiYang SongShenlong WangCharlie NashWilliam L. HamiltonDavid DuvenaudRaquel UrtasunRichard S. Zemel

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... (read more)

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