Degree distribution and scaling in the Connecting Nearest Neighbors model
Physics and Society
2016-08-11 v1 Social and Information Networks
Data Analysis, Statistics and Probability
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.
Keywords
Cite
@article{arxiv.1304.3375,
title = {Degree distribution and scaling in the Connecting Nearest Neighbors model},
author = {Boris Rudolf and Mária Markošová and Martin Čajági and Peter Tiňo},
journal= {arXiv preprint arXiv:1304.3375},
year = {2016}
}
Comments
21 pages, 3 figures