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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.