Highly clustered scale-free networks

Konstantin Klemm, Víctor M. Eguíluz

Research output: Contribution to journalArticle

282 Citations (Scopus)

Abstract

We propose a model for growing networks based on a finite memory of the nodes. The model shows stylized features of real-world networks: power-law distribution of degree, linear preferential attachment of new links, and a negative correlation between the age of a node and its link attachment rate. Notably, the degree distribution is conserved even though only the most recently grown part of the network is considered. As the network grows, the clustering reaches an asymptotic value larger than that for regular lattices of the same average connectivity and similar to the one observed in the networks of movie actors, coauthorship in science, and word synonyms. These highly clustered scale-free networks indicate that memory effects are crucial for a correct description of the dynamics of growing networks.

Original languageEnglish
Article number036123
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume65
Issue number3
DOIs
Publication statusPublished - Mar 2002
Externally publishedYes

Fingerprint

Scale-free Networks
Growing Networks
Preferential Attachment
Memory Effect
Power-law Distribution
Degree Distribution
Vertex of a graph
attachment
Connectivity
Clustering
Model

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Condensed Matter Physics
  • Statistical and Nonlinear Physics
  • Mathematical Physics

Cite this

Highly clustered scale-free networks. / Klemm, Konstantin; Eguíluz, Víctor M.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 65, No. 3, 036123, 03.2002.

Research output: Contribution to journalArticle

Klemm, Konstantin ; Eguíluz, Víctor M. / Highly clustered scale-free networks. In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics. 2002 ; Vol. 65, No. 3.
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