We employed the random graph theory approach to analyze data for seven species in the
protein-protein interaction database DIP. Several global topological parameters were used
to characterize the protein-protein interaction networks (PINs) for each species. The plots
of the logarithm of the node degree cumulative distribution Pcum(k) vs. the logarithm of
node degree k indicates that PINs follow the power law (Pcum(k) k? ). Good evidence by
correlation analysis supports the fact that the seven PINs are well approximated by scale-free
networks. We found that the logarithm of Cave(k) scales with k (i.e. Cave(k) k? ) for
E. coli and yeast. In particular, we determine that the E. coli and the yeast PINs are well
represented by the stochastic and deterministic hierarchical network models, respectively.
These results suggest that the hierarchical network model is a good description for certain
species’ PINs, but this may not be a universal feature across different species.