Sparse power-law network model for reliable statistical predictions based on sampled data
Resumen: 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.
Idioma: Inglés
DOI: 10.3390/e20040257
Año: 2018
Publicado en: ENTROPY 20, 4 (2018), 257 [17 pp]
ISSN: 1099-4300

Factor impacto JCR: 2.419 (2018)
Categ. JCR: PHYSICS, MULTIDISCIPLINARY rank: 28 / 81 = 0.346 (2018) - Q2 - T2
Factor impacto SCIMAGO:

Financiación: info:eu-repo/grantAgreement/ES/DGA/FENOL-GROUP
Financiación: info:eu-repo/grantAgreement/ES/MINECO/FIS2014-55867-P
Tipo y forma: Article (Published version)
Área (Departamento): Área Física Teórica (Dpto. Física Teórica)

Creative Commons You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.


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