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Título
Quantification of graph complexity based on the edge weight distribution balance: Application to brain networks
Autor
Año del Documento
2018
Editorial
World Scientific Publishing Company
Documento Fuente
International Journal of Neural Systems, Febrero, 2018, vol. 28 (1), pp. 1750032
Abstract
The aim of this study was to introduce a novel global measure of graph complexity: Shannon graph complexity (SGC). This measure was specifically developed for weighted graphs, but it can also be applied to binary graphs. The proposed complexity measure was designed to capture the interplay between two properties of a system: the ‘information’ (calculated by means of Shannon entropy) and the ‘order’ of the system (estimated by means of a disequilibrium measure). SGC is based on the concept that complex graphs should maintain an equilibrium between the aforementioned two properties, which can be measured by means of the edge weight distribution. In this study, SGC was assessed using four synthetic graph datasets and a real dataset, formed by electroencephalographic (EEG) recordings from controls and schizophrenia patients. SGC was compared with graph density (GD), a classical measure used to evaluate graph complexity. Our results showed that SGC is invariant with respect to GD and independent of node degree distribution. Furthermore, its variation with graph size (N) is close to zero for N>30. Results from the real dataset showed an increment in the weight distribution balance during the cognitive processing for both controls and schizophrenia patients, although these changes are more relevant for controls. Our findings revealed that SGC does not need a comparison with null-hypothesis networks constructed by a surrogate process. In addition, SGC results on the real dataset suggest that schizophrenia is associated with a deficit in the brain dynamic reorganization related to secondary pathways of the brain network.
Materias (normalizadas)
Artificial intelligence
Computer science
Palabras Clave
Graph theory
Brain networks
Brain complexity
Entropy
Revisión por pares
SI
Patrocinador
TEC2014-53196-R, FIS PI11/02203, PI15/00299, GRS 932/A/14 y GRS 1134/A/15
Version del Editor
Idioma
spa
Tipo de versión
info:eu-repo/semantics/submittedVersion
Derechos
openAccess
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