2004/01/31 by Jesús Gómez‐Gardeñes, Jesus Gomez-Gardenes, Yamir Moreno · 1 citation
Mathematics · Physics and Astronomy · #Artificial intelligence #Average path length #Cluster analysis #Clustering coefficient #Combinatorics #Complex Network Analysis Techniques #Complex network #Computer science #Contrast (vision) #Degree (music) #Degree distribution #Discrete mathematics #Field (mathematics) #Graph #Graph theory and applications #Mathematics #Opinion Dynamics and Social Influence #Physics #Pure mathematics #Scale (ratio) #Scale-free network #Shortest path problem #Statistical physics #Statistics #cond-mat.stat-mech
paper · pdf · doi:10.1103/physreve.69.037103
published as Physical Review E 69, 037103 (2004) · Revtex format. Final version appeared in PRE
openalex publication_date 2004/03/31 · arxiv created 2004/04/01 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The scale-free model of Barabási and Albert (BA) gave rise to a burst of activity in the field of complex networks. In this paper, we revisit one of the main assumptions of the model, the preferential attachment (PA) rule. We study a model in which the PA rule is applied to a neighborhood of newly created nodes and thus no global knowledge of the network is assumed. We numerically show that global properties of the BA model such as the connectivity distribution and the average shortest path length are quite robust when there is some degree of local knowledge. In contrast, other properties such as the clustering coefficient and degree-degree correlations differ and approach the values measured for real-world networks.