Network extreme eigenvalue: from mutimodal to scale-free networks.
Chaos
; 22(1): 013139, 2012 Mar.
Article
de En
| MEDLINE
| ID: mdl-22463015
ABSTRACT
The extreme eigenvalues of adjacency matrices are important indicators on the influence of topological structures to the collective dynamical behavior of complex networks. Recent findings on the ensemble averageability of the extreme eigenvalue have further authenticated its applicability to the study of network dynamics. However, the ensemble average of extreme eigenvalue has only been solved analytically up to the second order correction. Here, we determine the ensemble average of the extreme eigenvalue and characterize its deviation across the ensemble through the discrete form of random scale-free network. Remarkably, the analytical approximation derived from the discrete form shows significant improvement over previous results, which implies a more accurate prediction of the epidemic threshold. In addition, we show that bimodal networks, which are more robust against both random and targeted removal of nodes, are more vulnerable to the spreading of diseases.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Oscillométrie
/
Algorithmes
/
Dynamique non linéaire
/
Modèles neurologiques
/
Réseau nerveux
Type d'étude:
Prognostic_studies
Limites:
Animals
/
Humans
Langue:
En
Journal:
Chaos
Sujet du journal:
CIENCIA
Année:
2012
Type de document:
Article
Pays d'affiliation:
Singapour