Temporal Effects in the Growth of Networks
Ricardo Marino, PhD student LPTMS
This article explores a very interesting model in random graphs for preferential attachment with time-decaying properties, ideal for problems in which « popularity » decays over time (for instance, article citations).
Matúš Medo, Giulio Cimini, and Stanislao Gualdi
Abstract of the article : We show that to explain the growth of the citation network by preferential attachment (PA), one has to accept that individual nodes exhibit heterogeneous fitness values that decay with time. While previous PA-based models assumed either heterogeneity or decay in isolation, we propose a simple analytically treatable model that combines these two factors. Depending on the input assumptions, the resulting degree distribution shows an exponential, log-normal or power-law decay, which makes the model an apt candidate for modeling a wide range of real systems.