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arXiv · 2006.02879

Auto-decoding Graphs

Sohil Atul Shah·Vladlen Koltun
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Abstract

We present an approach to synthesizing new graph structures from empirically specified distributions. The generative model is an auto-decoder that learns to synthesize graphs from latent codes. The graph synthesis model is learned jointly with an empirical distribution over the latent codes. Graphs are synthesized using self-attention modules that are trained to identify likely connectivity patterns. Graph-based normalizing flows are used to sample latent codes from the distribution learned by the auto-decoder. The resulting model combines accuracy and scalability. On benchmark datasets of large graphs, the presented model outperforms the state of the art by a factor of 1.5 in mean accuracy and average rank across at least three different graph statistics, with a 2x speedup during inference.

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Sohil Atul Shah, Vladlen Koltun. 2020-06-04. Auto-decoding Graphs. https://arxiv.org/abs/2006.02879

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