Degree distribution and scaling in the Connecting Nearest Neighbors model

Abstract

We present a detailed analysis of the Connecting Nearest Neighbors (CNN) model by V\'azquez. We show that the degree distribution follows a power law, but the scaling exponent can vary with the parameter setting. Moreover, the correspondence of the growing version of the Connecting Nearest Neighbors (GCNN) model to the particular random walk model (PRW model) and recursive search model (RS model) is established.