RT info:eu-repo/semantics/article
T1 Quantification of graph complexity based on the edge weight distribution balance: Application to brain networks
A1 Gomez-Pilar, Javier
A1 Poza, Jesús
A1 Bachiller, Alejandro
A1 Gómez, Carlos
A1 Núñez, Pablo
A1 Lubeiro, Alba
A1 Molina, Vicente
A1 Hornero, Roberto
K1 Artificial intelligence
K1 Computer science
K1 Graph theory
K1 Brain networks
K1 Brain complexity
K1 Entropy
AB 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.
PB World Scientific Publishing Company
YR 2018
FD 2018
LK https://uvadoc.uva.es/handle/10324/65682
UL https://uvadoc.uva.es/handle/10324/65682
LA spa
NO International Journal of Neural Systems, Febrero, 2018, vol. 28 (1), pp. 1750032
DS UVaDOC
RD 14-oct-2024