The largest eigenvalue of the adjacency matrix of networks is a key quantity determining several important dynamical processes on complex networks. Based on this fact, we present a quantitative, objective characterization of the dynamical importance of network nodes and links in terms of their effect on the largest eigenvalue. We show how our characterization of the dynamical importance of nodes can be affected by degree-degree correlations and network community structure. We discuss how our characterization can be used to optimize techniques for controlling certain network dynamical processes and apply our results to real networks
A fundamental problem in the study of networks is the identification of important nodes. This is typ...
A fundamental problem in the study of networks is the identification of important nodes. This is typ...
Dynamical networks are powerful tools for modeling a broad range of complex systems, including finan...
The largest eigenvalue of a network’s adjacency matrix and its associated principal eigenvector are ...
The largest eigenvalue ? 1 of the adjacency matrix powerfully characterizes dynamic processes on net...
The largest eigenvalue ? 1 of the adjacency matrix powerfully characterizes dynamic processes on net...
The largest eigenvalue of a network’s adjacency matrix and its associated principal eigenvector are ...
The largest eigenvalue of a network’s adjacency matrix and its associated principal eigenvector are ...
Part 3: Reliability and ResilienceInternational audienceThe largest eigenvalue λ1 of the adjacency m...
Determining the effect of structural perturbations on the eigenvalue spectra of networks is an impor...
Dynamical networks are powerful tools for modeling a broad range of complex systems, including finan...
Dynamical networks are powerful tools for modeling a broad range of complex systems, including finan...
The spectral properties of the adjacency matrix, in particular its largest eigenvalue and the associ...
We study the effect of network structure on the dynamical response of networks of coupled discrete-s...
Dynamical networks are powerful tools for modeling a broad range of complex systems, including finan...
A fundamental problem in the study of networks is the identification of important nodes. This is typ...
A fundamental problem in the study of networks is the identification of important nodes. This is typ...
Dynamical networks are powerful tools for modeling a broad range of complex systems, including finan...
The largest eigenvalue of a network’s adjacency matrix and its associated principal eigenvector are ...
The largest eigenvalue ? 1 of the adjacency matrix powerfully characterizes dynamic processes on net...
The largest eigenvalue ? 1 of the adjacency matrix powerfully characterizes dynamic processes on net...
The largest eigenvalue of a network’s adjacency matrix and its associated principal eigenvector are ...
The largest eigenvalue of a network’s adjacency matrix and its associated principal eigenvector are ...
Part 3: Reliability and ResilienceInternational audienceThe largest eigenvalue λ1 of the adjacency m...
Determining the effect of structural perturbations on the eigenvalue spectra of networks is an impor...
Dynamical networks are powerful tools for modeling a broad range of complex systems, including finan...
Dynamical networks are powerful tools for modeling a broad range of complex systems, including finan...
The spectral properties of the adjacency matrix, in particular its largest eigenvalue and the associ...
We study the effect of network structure on the dynamical response of networks of coupled discrete-s...
Dynamical networks are powerful tools for modeling a broad range of complex systems, including finan...
A fundamental problem in the study of networks is the identification of important nodes. This is typ...
A fundamental problem in the study of networks is the identification of important nodes. This is typ...
Dynamical networks are powerful tools for modeling a broad range of complex systems, including finan...