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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Dynamics of Citation Networks
verfasst von : Gábor Csárdi
Erschienen in: Artificial Neural Networks – ICANN 2006
Verlag: Springer Berlin Heidelberg
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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.