Costa, L. da F.
Villas Boas, P. R.
Silva, F. N.
Rodrigues, F. A.
Total Authors: 4
 Univ Sao Paulo, Inst Fis Sao Carlos, BR-13560970 Sao Paulo - Brazil
 Univ Sao Paulo, Dept Matemat Aplicada & Estat, Inst Ciencias Matemat & Comp, BR-13560970 Sao Paulo - Brazil
Total Affiliations: 2
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT;
Web of Science Citations:
Complex networks exist in many areas of science such as biology, neuroscience, engineering, and sociology. The growing development of this area has led to the introduction of several topological and dynamical measurements, which describe and quantify the structure of networks. Such characterization is essential not only for the modeling of real systems but also for the study of dynamic processes that may take place in them. However, it is not easy to use several measurements for the analysis of complex networks, due to the correlation between them and the difficulty of their visualization. To overcome these limitations, we propose 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. We also demonstrate the efficiency and the universality of the proposed methods in a series of real-world networks in the three levels. (AU)