2002/05/31 by Sergei Maslov, Kim Sneppen, Alexei Zaliznyak · 8 citations
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Gene Regulatory Network Analysis #cond-mat.dis-nn #cond-mat.stat-mech
paper · pdf · doi:10.1016/j.physa.2003.06.002
published as Physica A 333, 529-540 (2004) · 6 pages, 7 figures
arxiv created 2002/11/06 · openalex publication_date 2003/10/11 · arxiv updated 2009/11/30 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
A general scheme for detecting and analyzing topological patterns in large complex networks is presented. In this scheme the network in question is compared with its properly randomized version that preserves some of its low-level topological properties. Statistically significant deviation of any measurable property of a network from this null model likely reflect its design principles and/or evolutionary history. We illustrate this basic scheme on the example of the correlation profile of the Internet quantifying correlations between connectivities of its neighboring nodes. This profile distinguishes the Internet from previously studied molecular networks with a similar scale-free connectivity distribution. We finally demonstrate that clustering in a network is very sensitive to both the connectivity distribution and its correlation profile and compare the clustering in the Internet to the appropriate null model.