GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

ICML 2018 Jiaxuan YouRex YingXiang RenWilliam L. HamiltonJure Leskovec

Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences. However, modeling complex distributions over graphs and then efficiently sampling from these distributions is challenging due to the non-unique, high-dimensional nature of graphs and the complex, non-local dependencies that exist between edges in a given graph... (read more)

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