Auto-decoding Graphs

• URL: http://arxiv.org/abs/2006.02879v1
• Date: Thu, 4 Jun 2020 14:23:01 GMT
• Title: Auto-decoding Graphs
• Authors: Sohil Atul Shah and Vladlen Koltun
• Abstract summary: The generative model is an auto-decoder that learns to synthesize graphs from latent codes. Graphs are synthesized using self-attention modules that are trained to identify likely connectivity patterns.
• Score: 91.3755431537592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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