Clustering Coefficients of Protein-Protein Interaction Networks
arXiv:0704.3748 · doi:10.1103/PhysRevE.75.051910
Abstract
The properties of certain networks are determined by hidden variables that are not explicitly measured. The conditional probability (propagator) that a vertex with a given value of the hidden variable is connected to k of other vertices determines all measurable properties. We study hidden variable models and find an averaging approximation that enables us to obtain a general analytical result for the propagator. Analytic results showing the validity of the approximation are obtained. We apply hidden variable models to protein-protein interaction networks (PINs) in which the hidden variable is the association free-energy, determined by distributions that depend on biochemistry and evolution. We compute degree distributions as well as clustering coefficients of several PINs of different species; good agreement with measured data is obtained. For the human interactome two different parameter sets give the same degree distributions, but the computed clustering coefficients differ by a factor of about two. This shows that degree distributions are not sufficient to determine the properties of PINs.
16 pages, 3 figures, in Press PRE uses pdflatex
References in corpus (11)
- Statistical mechanics of complex networks
- The structure and function of complex networks
- Assortative mixing in networks
- Mixing patterns in networks
- Dynamical and correlation properties of the Internet
- Large-scale topological and dynamical properties of Internet
- Class of correlated random networks with hidden variables
- A General Formalism for Inhomogeneous Random Graphs
- A simple physical model for scaling in protein-protein interaction networks
- Free-energy distribution of binary protein-protein binding suggests cross-species interactome differences
- Hierarchical and mixing properties of static complex networks emerging from the fluctuating classical random graphs