# Large deviations, basic information theorem for fitness preferential attachment random networks

No Thumbnail Available

## Date

2014

## Authors

## Journal Title

## Journal ISSN

## Volume Title

## Publisher

International journal of Statistics and Probability

## Abstract

For fitness preferential attachment random networks, we define the empirical degree and pair measure, which counts the number of vertices of a given degree and the number of edges with given fits, and the sample path empirical degree distribution. For the empirical degree and pair distribution for the fitness preferential attachment random networks, we find a large deviation upper bound. From this result we obtain a weak law of large numbers
for the empirical degree and pair distribution, and the basic information theorem or an asymptotic equipartition property for fitness preferential attachment random networks.

## Description

## Keywords

Large deviation upper bound, relative entropy, random network, random tree, random coloured graph, typed graph, asymptotic equipartition property