Preferential attachment with partial information

Résultats de recherche: Livre/Rapport/RevueAutre 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 originaleAnglais
EditeurNamur center for complex systems
Nombre de pages6
Volume9
Edition14
étatPublié - 4 sept. 2014

Série de publications

NomnaXys Technical Report Series
EditeurUniversity of Namur
Numéro14
Volume9

Citer ceci

Carletti, T., Gargiulo, F., & Lambiotte, R. (2014). Preferential attachment with partial information. (14 Ed.) (naXys Technical Report Series; Vol 9, Numéro 14). Namur center for complex systems.
Carletti, Timoteo ; Gargiulo, Floriana ; Lambiotte, Renaud. / Preferential attachment with partial information. 14 Ed. Namur center for complex systems, 2014. 6 p. (naXys Technical Report Series; 14).
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title = "Preferential attachment with partial information",
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.",
keywords = "complex networks, preferential attachment, statistical mechanics",
author = "Timoteo Carletti and Floriana Gargiulo and Renaud Lambiotte",
year = "2014",
month = "9",
day = "4",
language = "English",
volume = "9",
series = "naXys Technical Report Series",
publisher = "Namur center for complex systems",
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Carletti, T, Gargiulo, F & Lambiotte, R 2014, Preferential attachment with partial information. naXys Technical Report Series, Numéro 14, VOL. 9, VOL. 9, 14 edn, Namur center for complex systems.

Preferential attachment with partial information. / Carletti, Timoteo; Gargiulo, Floriana; Lambiotte, Renaud.

14 Ed. Namur center for complex systems, 2014. 6 p. (naXys Technical Report Series; Vol 9, Numéro 14).

Résultats de recherche: Livre/Rapport/RevueAutre rapport

TY - BOOK

T1 - Preferential attachment with partial information

AU - Carletti, Timoteo

AU - Gargiulo, Floriana

AU - Lambiotte, Renaud

PY - 2014/9/4

Y1 - 2014/9/4

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.

AB - 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.

KW - complex networks

KW - preferential attachment

KW - statistical mechanics

M3 - Other report

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BT - Preferential attachment with partial information

PB - Namur center for complex systems

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Carletti T, Gargiulo F, Lambiotte R. Preferential attachment with partial information. 14 Ed. Namur center for complex systems, 2014. 6 p. (naXys Technical Report Series; 14).