Preferential attachment with partial information

Timoteo Carletti; Floriana Gargiulo; Renaud Lambiotte

Preferential attachment with partial information

Timoteo Carletti, Floriana Gargiulo, Renaud Lambiotte

Namur Institute for Complex Systems

Résultats de recherche: Livre/Rapport/Revue › Autre rapport

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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 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.

langue originale	Anglais
Editeur	Namur center for complex systems
Nombre de pages	6
Volume	9
Edition	14
Etat de la publication	Publié - 4 sept. 2014

Série de publications

Nom	naXys Technical Report Series
Editeur	University of Namur
Numéro	14
Volume	9

Accès au document

TR naxys-09-14manuscrit soumis, 222 KB

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

Contient cette citation

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abstract = "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 apower 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{\'a}si-Albert model. Surprisingly, the latter feature holds true also when the fraction of known nodes goes asymptotically to zero. Analyticalresults are compared to direct simulations.",

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AU - Carletti, Timoteo

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N2 - 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 apower 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. Analyticalresults are compared to direct simulations.

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KW - complex networks

KW - preferential attachment

KW - statistical mechanics

M3 - Other report

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Preferential attachment with partial information

Résumé

Série de publications

Accès au document

Empreinte digitale

Projets

PAI n°P7/19 - DYSCO: Dynamical systems, control and optimization (DYSCO)

Contient cette citation