TY - JOUR
T1 - A robust method for fitting degree distributions of complex networks
AU - Mannion, Shane
AU - MacCarron, Pádraig
N1 - Publisher Copyright:
© 2023 The Author(s). Published by Oxford University Press.
PY - 2023/8/1
Y1 - 2023/8/1
N2 - This work introduces a method for fitting to the degree distributions of complex network datasets, such that the most appropriate distribution from a set of candidate distributions is chosen while maximizing the portion of the distribution to which the model is fit. Current methods for fitting to degree distributions in the literature are inconsistent and often assume a priori what distribution the data are drawn from. Much focus is given to fitting to the tail of the distribution, while a large portion of the distribution below the tail is ignored. It is important to account for these low degree nodes, as they play crucial roles in processes such as percolation. Here, we address these issues, using maximum likelihood estimators to fit to the entire dataset or close to it. This methodology is applicable to any network dataset (or discrete empirical dataset), and we test it on over 25 network datasets from a wide range of sources, achieving good fits in all but a few cases. We also demonstrate that numerical maximization of the likelihood performs better than commonly used analytical approximations. In addition, we have made available a Python package which can be used to apply this methodology.
AB - This work introduces a method for fitting to the degree distributions of complex network datasets, such that the most appropriate distribution from a set of candidate distributions is chosen while maximizing the portion of the distribution to which the model is fit. Current methods for fitting to degree distributions in the literature are inconsistent and often assume a priori what distribution the data are drawn from. Much focus is given to fitting to the tail of the distribution, while a large portion of the distribution below the tail is ignored. It is important to account for these low degree nodes, as they play crucial roles in processes such as percolation. Here, we address these issues, using maximum likelihood estimators to fit to the entire dataset or close to it. This methodology is applicable to any network dataset (or discrete empirical dataset), and we test it on over 25 network datasets from a wide range of sources, achieving good fits in all but a few cases. We also demonstrate that numerical maximization of the likelihood performs better than commonly used analytical approximations. In addition, we have made available a Python package which can be used to apply this methodology.
KW - complex networks
KW - degree distribution
KW - graph
KW - maximum likelihood estimators
KW - network
KW - power law
UR - http://www.scopus.com/inward/record.url?scp=85167604242&partnerID=8YFLogxK
U2 - 10.1093/comnet/cnad023
DO - 10.1093/comnet/cnad023
M3 - Article
AN - SCOPUS:85167604242
SN - 2051-1310
VL - 11
JO - Journal of Complex Networks
JF - Journal of Complex Networks
IS - 4
M1 - cnad023
ER -