Preferential attachment with partial information

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Résumé

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.
langue originaleAnglais
Pages (de - à)18
Nombre de pages5
journalEuropean Physical Journal B
Volume88
Les DOIs
Etat de la publicationPublié - 14 janv. 2015

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