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
The continuous advancements in reinforcement learning are enabling its application to increasingly complex and theoretical problems, pushing the boundaries of what AI can model and optimize.
This development allows for more precise control and understanding of complex network structures, which has implications across various scientific and engineering disciplines from materials science to social networks.
The ability to sample microcanonical graph ensembles with hard constraints effectively opens new avenues for designing systems with exact, rather than approximate, structural properties.
- · AI researchers
- · Network scientists
- · Materials science
- · Drug discovery
- · Traditional sampling methods
Improved understanding and design of complex systems from biological networks to infrastructure.
Accelerated discovery of novel materials or molecular structures with specific, desired properties.
Enhanced AI agents capable of designing and optimizing complex real-world systems with precise structural control.
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