Mining and analyzing the topological structure of protein-protein interaction networks

Abstract

We report a comprehensive evaluation of the topological structure of protein-protein interaction (PPI) networks by mining and analyzing graphs constructed from the publicly available popular data sets to the bioinformatics research community. We compare the topology of these networks across different species, at different confidence levels, and from different experimental systems. Our results confirm the well-accepted claim that the degree distribution follows a power law. However, further statistical analysis shows that the residues are not independent on the fit values, indicating that the power law model may be inadequate. Our results also show that the dependence of the average clustering coefficient on the vertices degree is far from a power law, contradicting many published results. For the first time, we report that the average vertex density exhibits a strong power law dependence on the vertices degree for all the networks studied, regardless of species, confidence levels, and experimental systems.