A network reduction method inducing scale-free degree distribution

Abstract : This paper deals with the problem of graph reduction towards a scale-free graph while preserving a consistency with the initial graph. This problem is formulated as a minimization problem and to this end we define a metric to measure the scale-freeness of a graph and another metric to measure the similarity between two graphs with different dimensions, based on spectral centrality. We also want to ensure that if the initial network is a flow network, the reduced network preserves this property. We explore the optimization problem and, based on the gained insights, we derive an algorithm allowing to find an approximate solution. Finally, the effectiveness of the algorithm is shown through a simulation on a Manhattan-like network.
Type de document :
Communication dans un congrès
ECC 2018 - European Control Conference, Jun 2018, Limassol, Cyprus. pp.1-6, 2018, 〈http://www.ecc18.eu/〉
Liste complète des métadonnées

Littérature citée [26 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01632482
Contributeur : Nicolas Martin <>
Soumis le : jeudi 19 avril 2018 - 14:46:18
Dernière modification le : samedi 21 avril 2018 - 01:27:40

Fichier

ECC18_0364_FI.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01632482, version 3

Collections

Citation

Nicolas Martin, Paolo Frasca, Carlos Canudas de Wit. A network reduction method inducing scale-free degree distribution. ECC 2018 - European Control Conference, Jun 2018, Limassol, Cyprus. pp.1-6, 2018, 〈http://www.ecc18.eu/〉. 〈hal-01632482v3〉

Partager

Métriques

Consultations de la notice

15

Téléchargements de fichiers

4