70304
20190709135551.0
doi
10.3390/e20040257
sideral
105779
ART-2018-105779
eng
Kartun-Giles, A.P.
Sparse power-law network model for reliable statistical predictions based on sampled data
2018
Access copy available to the general public
Unrestricted
A projective network model is a model that enables predictions to be made based on a subsample of the network data, with the predictions remaining unchanged if a larger sample is taken into consideration. An exchangeable model is a model that does not depend on the order in which nodes are sampled. Despite a large variety of non-equilibrium (growing) and equilibrium (static) sparse complex network models that are widely used in network science, how to reconcile sparseness (constant average degree) with the desired statistical properties of projectivity and exchangeability is currently an outstanding scientific problem. Here we propose a network process with hidden variables which is projective and can generate sparse power-law networks. Despite the model not being exchangeable, it can be closely related to exchangeable uncorrelated networks as indicated by its information theory characterization and its network entropy. The use of the proposed network process as a null model is here tested on real data, indicating that the model offers a promising avenue for statistical network modelling.
info:eu-repo/grantAgreement/ES/DGA/FENOL-GROUP
info:eu-repo/grantAgreement/ES/MINECO/FIS2014-55867-P
info:eu-repo/semantics/openAccess
by
http://creativecommons.org/licenses/by/3.0/es/
2.419
2018
PHYSICS, MULTIDISCIPLINARY
28 / 81 = 0.346
2018
Q2
T2
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Krioukov, D.
Gleeson, J.P.
Moreno, Y.
Universidad de Zaragoza
(orcid)0000-0002-0895-1893
Bianconi, G.
2004
405
Universidad de Zaragoza
Dpto. Física Teórica
Área Física Teórica
20, 4 (2018), 257 [17 pp]
Entropy
ENTROPY
1099-4300
463806
http://zaguan.unizar.es/record/70304/files/texto_completo.pdf
Versión publicada
99613
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articulos
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2019-07-09-12:12:09
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