Reinforcement Learning for Microcanonical Graph Ensemble with Assortativity Constraints
A new paper introduces the Deep Microcanonical Graph Generator (DMGG), a reinforcement learning framework for generating graphs with specific assortativity constraints. This approach allows for exact control over graph structures, overcoming limitations of traditional methods. The DMGG significantly accelerates the generation process while maintaining configurational diversity, paving the way for better understanding of structure-function relationships in networks.
- ▪The DMGG transforms graphs through degree-preserving rewirings to achieve prescribed assortativity.
- ▪It employs a policy-guided search instead of traditional Metropolis-Hastings dynamics.
- ▪The framework accelerates graph generation by at least an order of magnitude while preserving diversity.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23285 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
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Computer Science > Machine Learning arXiv:2605.23285 (cs) [Submitted on 22 May 2026] Title:Reinforcement Learning for Microcanonical Graph Ensemble with Assortativity Constraints Authors:Hoyun Choi, Junghyo Jo, Deok-Sun Lee View a PDF of the paper titled Reinforcement Learning for Microcanonical Graph Ensemble with Assortativity Constraints, by Hoyun Choi and 2 other authors View PDF HTML (experimental) Abstract:How network structure determines function is a fundamental question, and it can be investigated by graph ensembles with precisely controlled structural properties. Canonical approaches, formulated as exponential random graph models (ERGMs), enforce constraints only in expectation, allowing individual realizations to fluctuate around the target.
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