Preferential attachment with partial information
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- Carletti, T., Gargiulo, F. & Lambiotte, R. Eur. Phys. J. B (2015) 88: 18. doi:10.1140/epjb/e2014-50595-0
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.