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: Proceedings of the 27th International Conference on Arti- ficial Neural Networks (ICANN), Rhodes, Greece (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.