000070304 001__ 70304
000070304 005__ 20190709135551.0
000070304 0247_ $$2doi$$a10.3390/e20040257
000070304 0248_ $$2sideral$$a105779
000070304 037__ $$aART-2018-105779
000070304 041__ $$aeng
000070304 100__ $$aKartun-Giles, A.P.
000070304 245__ $$aSparse power-law network model for reliable statistical predictions based on sampled data
000070304 260__ $$c2018
000070304 5060_ $$aAccess copy available to the general public$$fUnrestricted
000070304 5203_ $$aA 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.
000070304 536__ $$9info:eu-repo/grantAgreement/ES/DGA/FENOL-GROUP$$9info:eu-repo/grantAgreement/ES/MINECO/FIS2014-55867-P
000070304 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000070304 590__ $$a2.419$$b2018
000070304 591__ $$aPHYSICS, MULTIDISCIPLINARY$$b28 / 81 = 0.346$$c2018$$dQ2$$eT2
000070304 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000070304 700__ $$aKrioukov, D.
000070304 700__ $$aGleeson, J.P.
000070304 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Y.$$uUniversidad de Zaragoza
000070304 700__ $$aBianconi, G.
000070304 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000070304 773__ $$g20, 4 (2018), 257 [17 pp]$$pEntropy$$tENTROPY$$x1099-4300
000070304 8564_ $$s463806$$uhttp://zaguan.unizar.es/record/70304/files/texto_completo.pdf$$yVersión publicada
000070304 8564_ $$s99613$$uhttp://zaguan.unizar.es/record/70304/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000070304 909CO $$ooai:zaguan.unizar.es:70304$$particulos$$pdriver
000070304 951__ $$a2019-07-09-12:12:09
000070304 980__ $$aARTICLE