2008/06/19 by Lazaros K. Gallos, Chaoming Song, Hernán A. Makse +1
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Opinion Dynamics and Social Influence #physics.soc-ph
paper · pdf · doi:10.1103/physrevlett.100.248701
published as Phys. Rev. Lett. 100, 248701 (2008) · 5 pages, 4 figures
openalex publication_date 2008/06/19 · arxiv created 2008/08/15 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Connectivity correlations play an important role in the structure of scale-free networks. While several empirical studies exist, there is no general theoretical analysis that can explain the largely varying behavior of real networks. Here, we use scaling theory to quantify the degree of correlations in the particular case of networks with a power-law degree distribution. These networks are classified in terms of their correlation properties, revealing additional information on their structure. For instance, the studied social networks and the Internet at the router level are clustered around the line of random networks, implying a strongly connected core of hubs. On the contrary, some biological networks and the WWW exhibit strong anticorrelations. The present approach can be used to study robustness or diffusion, where we find that anticorrelations tend to accelerate the diffusion process.