Open AccessConstructing a Watts-Strogatz network from a small-world network with symmetric degree distribution
Author(s) -
Mozart B.C. Menezes,
Seokjin Kim,
Rongbing Huang
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0179120
Subject(s) - degree distribution , degree (music) , scale free network , clustering coefficient , replicate , computer science , average path length , cluster analysis , sample (material) , sample size determination , small world network , complex network , hierarchical network model , scale (ratio) , statistics , mathematics , artificial intelligence , physics , theoretical computer science , graph , quantum mechanics , shortest path problem , world wide web , acoustics , thermodynamics
Though the small-world phenomenon is widespread in many real networks, it is still challenging to replicate a large network at the full scale for further study on its structure and dynamics when sufficient data are not readily available. We propose a method to construct a Watts-Strogatz network using a sample from a small-world network with symmetric degree distribution. Our method yields an estimated degree distribution which fits closely with that of a Watts-Strogatz network and leads into accurate estimates of network metrics such as clustering coefficient and degree of separation. We observe that the accuracy of our method increases as network size increases.