Preferential attachment with partial information

Timoteo Carletti, Floriana Gargiulo and Renaud Lambiotte ()

The European Physical Journal B: Condensed Matter and Complex Systems, 2015, vol. 88, issue 1, 1-5

Abstract: We propose a preferential attachment model for network growth where new entering nodes have a partial information about the state of the network. Our main result is that the presence of bounded information modifies the degree distribution by introducing an exponential tail, while it preserves a power law behaviour over a finite small range of degrees. On the other hand, unbounded information is sufficient to let the network grow as in the standard Barabási-Albert model. Surprisingly, the latter feature holds true also when the fraction of known nodes goes asymptotically to zero. Analytical results are compared to direct simulations. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2015

Keywords: Statistical and Nonlinear Physics (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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DOI: 10.1140/epjb/e2014-50595-0

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