Preferential attachment with partial information

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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.
Original languageEnglish
Pages (from-to)18
Number of pages5
JournalEuropean Physical Journal B
Publication statusPublished - 14 Jan 2015


  • complex networks
  • preferential attachment
  • statistical mechanics


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