Reinforcement Learning for Microcanonical Graph Ensemble with Assortativity Constraints

arXiv:2605.23285v1 Announce Type: new 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. Conversely, microcanonical ensembles impose hard constraints exactly, but practical sampling methods beyond fixing the degree sequence have remained out of reach. Here we introduce the Deep Microcanonical

Source: arXiv cs.LG — read the full report at the original publisher.

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