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arXiv · 1409.1013

Preferential attachment with partial information

Timoteo Carletti·Floriana Gargiulo·Renaud Lambiotte
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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 Barab\'asi-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.

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Timoteo Carletti, Floriana Gargiulo, Renaud Lambiotte. 2014-09-03. Preferential attachment with partial information. https://doi.org/10.1140/epjb%2Fe2014-50595-0

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