Spectral density of random graphs: convergence properties and application in model fitting

Suzana de Siqueira Santos (1, 2) , André Fujita (1) , Catherine Matias (3)

1 USP - Universidade de São Paulo = University of São Paulo
2 FGV - Fundacao Getulio Vargas [Rio de Janeiro]
3 LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation

Random graph models are used to describe the complex structure of real-world networks in diverse fields of knowledge. Studying their behavior and fitting properties are still critical challenges, that in general, require model specific techniques. An important line of research is to develop generic methods able to fit and select the best model among a collection. Approaches based on spectral density (i.e., distribution of the graph adjacency matrix eigenvalues) are appealing for that purpose: they apply to different random graph models. Also, they can benefit from the theoretical background of random matrix theory. This work investigates the convergence properties of model fitting procedures based on the graph spectral density and the corresponding cumulative distribution function. We also review results on the convergence of the spectral density for the most widely used random graph models. Moreover, we explore through simulations the limits of these graph spectral density convergence results, particularly in the case of the block model, where only partial results have been established.

Mots clés: random graphs; spectral density; model fitting; model selection; convergence

Domaines: Théorie [stat.TH]

Suzana de Siqueira Santos, André Fujita, Catherine Matias. Spectral density of random graphs: convergence properties and application in model fitting. 2021. ⟨hal-03133261v1⟩