000095854 001__ 95854
000095854 005__ 20210902121618.0
000095854 0247_ $$2doi$$a10.1088/1367-2630/ab687c
000095854 0248_ $$2sideral$$a116419
000095854 037__ $$aART-2020-116419
000095854 041__ $$aeng
000095854 100__ $$aAlves, Luiz GA
000095854 245__ $$aCentrality anomalies in complex networks as a result of model over-simplification
000095854 260__ $$c2020
000095854 5060_ $$aAccess copy available to the general public$$fUnrestricted
000095854 5203_ $$aTremendous advances have been made in our understanding of the properties and evolution of complex networks. These advances were initially driven by information-poor empirical networks and theoretical analysis of unweighted and undirected graphs. Recently, information-rich empirical data complex networks supported the development of more sophisticated models that include edge directionality and weight properties, and multiple layers. Many studies still focus on unweighted undirected description of networks, prompting an essential question: how to identify when a model is simpler than it must be? Here, we argue that the presence of centrality anomalies in complex networks is a result of model over-simplification. Specifically, we investigate the well-known anomaly in betweenness centrality for transportation networks, according to which highly connected nodes are not necessarily the most central. Using a broad class of network models with weights and spatial constraints and four large data sets of transportation networks, we show that the unweighted projection of the structure of these networks can exhibit a significant fraction of anomalous nodes compared to a random null model. However, the weighted projection of these networks, compared with an appropriated null model, significantly reduces the fraction of anomalies observed, suggesting that centrality anomalies are a symptom of model over-simplification. Because lack of information-rich data is a common challenge when dealing with complex networks and can cause anomalies that misestimate the role of nodes in the system, we argue that sufficiently sophisticated models be used when anomalies are detected.
000095854 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E36-17R$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/FIS2017-87519-P$$9info:eu-repo/grantAgreement/ES/MINECO/FIS2014-55867
000095854 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000095854 590__ $$a3.729$$b2020
000095854 591__ $$aPHYSICS, MULTIDISCIPLINARY$$b22 / 85 = 0.259$$c2020$$dQ2$$eT1
000095854 592__ $$a1.584$$b2020
000095854 593__ $$aPhysics and Astronomy (miscellaneous)$$c2020$$dQ1
000095854 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000095854 700__ $$0(orcid)0000-0002-1192-8707$$aAleta, Alberto
000095854 700__ $$aRodrigues, FA
000095854 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Yamir$$uUniversidad de Zaragoza
000095854 700__ $$aNunes Amaral, Luis A.
000095854 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000095854 773__ $$g22, 1 (2020), 013043 [12 pp]$$pNew j. phys.$$tNew Journal of Physics$$x1367-2630
000095854 8564_ $$s1143037$$uhttps://zaguan.unizar.es/record/95854/files/texto_completo.pdf$$yVersión publicada
000095854 8564_ $$s102113$$uhttps://zaguan.unizar.es/record/95854/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000095854 909CO $$ooai:zaguan.unizar.es:95854$$particulos$$pdriver
000095854 951__ $$a2021-09-02-08:45:31
000095854 980__ $$aARTICLE