  Networks have been used to model many real-world phenomena to better
understand the phenomena and to guide experiments in order to predict their
behavior. Since incorrect models lead to incorrect predictions, it is vital to
have a correct model. As a result, new techniques and models for analyzing and
modeling real-world networks have recently been introduced. One example of
large and complex networks involves protein-protein interaction (PPI) networks.
We demonstrate that the currently popular scale-free model of PPI networks
fails to fit the data in several respects. We show that a random geometric
model provides a much more accurate model of the PPI data.
