Random Network Model Discrimination

To analyze structural data, networks have become more and more a key technology in SystemsBiology. Examples of underlying networks are protein-protein-interactions (PPI). Recently advance-ments in experimental biotechnology have produced large number of PPI data. The topology of PPInetworks is believed to have a strong link to their functions. Hence, the abundance of PPI data formany organisms stimulates the developments of computational techniques for modeling of networks,?nding a representative models for PPI networks will improve our understanding of the cell just likea model of gravity has helped us understand planetary motion. To decide a model is representative,quantitative comparisons of model networks to real ones are needed. However exact network compar-ison is computationally intractable and therefore many heuristic descriptors have been used instead.But now little is known what network topological characteristics can be captured using topologicaldescriptors .In this paper, we investigate mathematical and distributional properties of selected descriptorsand based on these descriptors introduced a classi?cation rule to distinguish barabasi model andgeometric model for PPIs.