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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 originale | Anglais |
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Pages (de - à) | 18 |
Nombre de pages | 5 |
journal | European Physical Journal B |
Volume | 88 |
Les DOIs | |
Etat de la publication | Publié - 14 janv. 2015 |
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PAI n°P7/19 - DYSCO: Dynamical systems, control and optimization (DYSCO)
Winkin, J., Blondel, V., Vandewalle, J., Pintelon, R., Sepulchre, R., Vande Wouwer, A. & Sartenaer, A.
1/04/12 → 30/09/17
Projet: Recherche