P. R. Villas Boas

TL;DR: In this paper, the authors present a survey of topological features of complex networks, including trajectories in several measurement spaces, correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification.

TL;DR: This article presents a survey of measurements capable of expressing the most relevant topological features of complex networks and includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements.

TL;DR: This work proposes an effective and comprehensive approach for the analysis of complex networks, which allows the visualization of several measurements in a few projections that contain the largest data variance and the classification of networks into three levels of detail, vertices, communities, and the global topology.

TL;DR: In this paper, the sensitivity of complex network measurements to perturbations in the topology of the network is quantified in terms of the relative entropy of the respective distributions, and the measurements allowing the best balance of stability and discriminability are identified with respect to each type of perturbation.

TL;DR: The path-star transformation (in its uniform and preferential versions) is introduced as a means to model such network transformations where paths give rise to stars of connectivity and it is shown that while the US highways network adheres closely to a geographical network model, its path- star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.