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An information theoretic approach inspired by quantum statistical mechanicswas recently proposed as a means to optimize network models and to assess theirlikelihood against synthetic and real-world networks. Importantly, this methoddoes not rely on specific topological features or network descriptors, butleverages entropy-based measures of network distance. Entertaining the analogywith thermodynamics, we provide a physical interpretation of modelhyperparameters and propose analytical procedures for their estimate. Theseresults enable the practical application of this novel and powerful frameworkto network model inference. We demonstrate this method in synthetic networksendowed with a modular structure, and in real-world brain connectivitynetworks.

Thermodynamics of network model fitting with spectral entropies