Growing scale-free networks with small-world behavior

2001/07/30 by Konstantin Klemm, Victor M. Eguiluz, Victor M. Eguı́luz · 3 citations
Mathematics · Physics and Astronomy · #Complex Network Analysis Techniques #Stochastic processes and statistical mechanics #Theoretical and Computational Physics #cond-mat

paper · pdf · doi:10.1103/physreve.65.057102

published as Phys. Rev. E 65, 057102 (2002) · 4 pages, 4 figures

arxiv created 2001/07/30 · openalex publication_date 2002/05/08 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

In the context of growing networks, we introduce a simple dynamical model that unifies the generic features of real networks: scale-free distribution of degree and the small-world effect. While the average shortest path length increases logarithmically as in random networks, the clustering coefficient assumes a large value independent of system size. We derive analytical expressions for the clustering coefficient in two limiting cases: random [C approximately (ln N)(2)/N] and highly clustered (C=5/6) scale-free networks.

Citations

• Emergence of scaling in random networks
• Statistical mechanics of complex networks
• The large-scale organization of metabolic networks

Cited by

• Transition from fractal to non-fractal scalings in growing scale-free networks
• Universal scaling behavior of clustering coefficient induced by deactivation mechanism
• Topology and correlations in structured scale-free networks