Identifying and quantifying dissimilarities among graphs is a fundamental and challenging
problem of practical importance in many fields of science. Current methods of network …

Complex network theory provides a powerful toolbox for studying the structure of statistical
interrelationships between multiple time series in various scientific disciplines. In this work …

We present here two promising techniques for the application of the complex network
approach to continuous spatio-temporal systems that have been developed in the last …

Eigen techniques such as empirical orthogonal function (EOF) or coupled pattern
(CP)/maximum covariance analysis have been frequently used for detecting patterns in …

Complex networks theory have gained wider applicability since methods for transformation
of time series to networks were proposed and successfully tested. In the last few years …

Understanding diffusive processes in networks is a significant challenge in complexity
science. Networks possess a diffusive potential that depends on their topological …

Quantifying similarities/dissimilarities among different graph models and studying the
irregularity (heterogeneity) of graphs in graphs and complex networks are one of the …

This work proposes the use of Information Theory for the characterization of vehicles
behavior through their velocities. Three public data sets were used:(i) Mobile Century data …

Many natural systems can be represented by complex networks of dynamical units with
modular structure in the form of communities of densely interconnected nodes. Unraveling …

Experimentally observed networks of interacting dynamical systems are inferred from
recorded multivariate time series by evaluating a statistical measure of dependence, usually …