Preferential attachment with partial information

Research output: Book/Report/JournalOther report

16 Downloads (Pure)


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.
Original languageEnglish
PublisherNamur center for complex systems
Number of pages6
Publication statusPublished - 4 Sep 2014

Publication series

NamenaXys Technical Report Series
PublisherUniversity of Namur


  • complex networks
  • preferential attachment
  • statistical mechanics


Dive into the research topics of 'Preferential attachment with partial information'. Together they form a unique fingerprint.

Cite this