DMGG uses reinforcement learning to generate microcanonical graph ensembles with exact assortativity constraints via degree-preserving rewirings, claiming faster generation and better diversity than ERGM approaches.
In: Workshop on Theoretical Foundations and Applications of Deep Generative Mod- els at the 35th International Conference on Machine Learning (ICML) (2018)
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Reinforcement Learning for Microcanonical Graph Ensemble with Assortativity Constraints
DMGG uses reinforcement learning to generate microcanonical graph ensembles with exact assortativity constraints via degree-preserving rewirings, claiming faster generation and better diversity than ERGM approaches.