Clustering of random scale-free networks

We derive the finite-size dependence of the clustering coefficient of scale-free random graphs generated by the configuration model with degree distribution exponent 2<γ<3. Degree heterogeneity increases the presence of triangles in the network up to levels that compare to those found in many...

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Veröffentlicht in:Physical review. E, Statistical, nonlinear, and soft matter physics Jg. 86; H. 2; S. 026120 - 26124
Hauptverfasser: Colomer-de-Simon, Pol, Boguñá, Marián
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States American Physical Society 30.08.2012
Schlagworte:
Algorithms
Cluster Analysis
Complex systems
Computer networks
Computer Simulation
Markov Chains
Models, Neurological
Models, Statistical
Models, Theoretical
Nerve Net - physiology
Physics - methods
Probability
Sistemes complexos
Thermodynamics
Xarxes d'ordinadors
ISSN:1539-3755, 1550-2376, 1550-2376
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:We derive the finite-size dependence of the clustering coefficient of scale-free random graphs generated by the configuration model with degree distribution exponent 2<γ<3. Degree heterogeneity increases the presence of triangles in the network up to levels that compare to those found in many real networks even for extremely large nets. We also find that for values of γ≈2, clustering is virtually size independent and, at the same time, becomes a de facto non-self-averaging topological property. This implies that a single-instance network is not representative of the ensemble even for very large network sizes.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1539-3755
1550-2376
1550-2376
DOI:10.1103/PhysRevE.86.026120