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Analyzing complex networks evolution through Information Theory quantifiers |
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| Authors: | Laura C Carpi Osvaldo A Rosso Patricia M Saco Martín Gómez Ravetti |
| Institution: | a Civil, Surveying and Environmental Engineering, The University of Newcastle, University Drive, Callaghan NSW 2308, Australia b Departamento de Física, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte (31270-901), MG, Brazil c Chaos & Biology Group, Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellón II, Ciudad Universitaria, 1428 Ciudad de Buenos Aires, Argentina d Departamento de Hidráulica, Facultad de Ciencias Exactas, Ingeniería y Agrimensura, Universidad Nacional de Rosario, Avenida Pellegrini 250, Rosario, Argentina e Departamento de Engenharia de Produção, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627, Belo Horizonte (31270-901), MG, Brazil |
| Abstract: | A methodology to analyze dynamical changes in complex networks based on Information Theory quantifiers is proposed. The square root of the Jensen-Shannon divergence, a measure of dissimilarity between two probability distributions, and the MPR Statistical Complexity are used to quantify states in the network evolution process. Three cases are analyzed, the Watts-Strogatz model, a gene network during the progression of Alzheimer's disease and a climate network for the Tropical Pacific region to study the El Niño/Southern Oscillation (ENSO) dynamic. We find that the proposed quantifiers are able not only to capture changes in the dynamics of the processes but also to quantify and compare states in their evolution. |
| Keywords: | Complex networks Jensen-Shannon divergence Statistical complexity |
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