Chapter

Artificial Neural Networks – ICANN 2006

Volume 4131 of the series Lecture Notes in Computer Science pp 698-709

Dynamics of Citation Networks

  • Gábor CsárdiAffiliated withDepartment of Biophysics, KFKI Research Institute for Particle and Nuclear Physics of the Hungarian Academy of SciencesCenter for Complex Systems Studies, Kalamazoo College

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Abstract

The aim of this paper is to give theoretical and experimental tools for measuring the driving force in evolving complex networks. First a discrete-time stochastic model framework is introduced to state the question of how the dynamics of these networks depend on the properties of the parts of the system. Then a method is presented to determine this dependence in the possession of the required data about the system. This measurement method is applied to the citation network of high energy physics papers to extract the in-degree and age dependence of the dynamics. It is shown that the method yields close to “optimal” results.