Large deviation principles for empirical measures of colored random graphs

Abstract

For any finite colored graph we define the empirical neighborhood measures, which counts the number of vertices of a given color connected to a given number of vertices of each color, and the empirical pair measure, which counts the number of edges connecting each pair of colors. For a class of models of sparse colored random graphs, we prove large deviation principles for these empirical measures In the weak topology. The rate functions governing our large deviation principles can be expressed explicitly in terms of relative entropies. We derive a large deviation principle for the degrees distribution of ErÖÖs-Réenyl graphs near criticality.