LARGE DEVIATION PRINCIPLES FOR EMPIRICAL MEASURES OF COLORED RANDOM GRAPHS
成果类型：
Article
署名作者：
Doku-Amponsah, Kwabena; Moerters, Peter
署名单位：
University of Ghana; University of Bath
刊物名称：
ANNALS OF APPLIED PROBABILITY
ISSN/ISSBN：
1050-5164
DOI：
10.1214/09-AAP647
发表日期：
2010
页码：
1989-2021
关键词：
摘要：
For any finite colored graph we define the empirical neighborhood measure, 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 degree distribution of Erdos-Renyi graphs near criticality.