Predicting spin‑glass states in networks with correlated disorder

Develop a predictive framework to determine spin‑glass states in complex networks when interactions are drawn from ensembles with correlated disorder, moving beyond uncorrelated disorder assumptions.

Background

The authors point out that correlated disorder ensembles introduce additional structure that is not captured by classic uncorrelated models, making the prediction of spin‑glass states in networked architectures particularly challenging.

They motivate a signed‑Laplacian perspective to disentangle topology and antagonistic interactions, but underline that forecasting spin‑glass states under correlated disorder remains an outstanding task.