Reinforcement Learning for Graph Generation under a Hard Assortativity Constraint
Generating graph ensembles with precisely controlled structural properties is central to investigating how network structure shapes function. Canonical ensembles impose constraints only in expectation (soft constraints), letting individual realizations fluctuate around the target, whereas enforcing hard constraints with prescribed precision in every realization remains challenging beyond fixing the degree sequence. Here we show that a reinforcement learning framework can drive a graph through degree-preserving rewirings to satisfy a prescribed assortativity, which characterizes the degree--degree correlation of adjacent nodes. By replacing the entropically dominated Metropolis--Hastings r...
| Authors | Hoyun Choi, Junghyo Jo, Deok-Sun Lee |
|---|---|
| Primary category | cs.LG |
| Published | 22 May 2026 |
| Last updated at source | 17 September 2026 |
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