Analyzing complex networks evolution through Information Theory quantifiers

Title: Analyzing complex networks evolution through Information Theory quantifiers
Creator: Carpi, Laura C.; Rosso, Osvaldo A.; Saco, Patricia M.; Ravetti, Martín Gómez
Relation: Physics Letters A Vol. 375, Issue 4, p. 801-804
Publisher Link: http://dx.doi.org/10.1016/j.physleta.2010.12.038
Publisher: Elsevier
Resource Type: journal article
Date: 2011
Description: 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.
Subject: complex networks; Jensen–Shannon divergence; statistical complexity; Information Theory quantifiers
Identifier: http://hdl.handle.net/1959.13/936277
Identifier: uon:12263
Identifier: ISSN:0375-9601
Language: eng
Reviewed

		Thumbnail	File	Description	Size	Format