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Preferential attachment with partial information


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.

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Correspondence to Renaud Lambiotte.

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Carletti, T., Gargiulo, F. & Lambiotte, R. Preferential attachment with partial information. Eur. Phys. J. B 88, 18 (2015).

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  • Statistical and Nonlinear Physics