Evolving networks through deletion and duplication
  We introduce a minimalistic model based on dynamic node deletion and node duplication with heterodimerisation. The model is intended to capture the essential features of the evolution of protein interaction networks. We derive an exact two-step rate equation to describe the evolution of the degree distribution. We present results for the case of a fixed-size network. The results are based on the exact numerical solution to the rate equation which are consistent with Monte Carlo simulations of the model's dynamics. Power-law degree distributions with apparent exponents $<1$ were observed for generic parameter choices. However, a proper finite-size scaling analysis revealed that the actual critical exponent in such cases is equal to 1. We present a mean-field argument to determine the asymptotic value of the average degree, illustrating the existence of an attractive fixed point, and corroborate this result with numerical simulations of the first moment of the degree distribution as described by the two-step rate equation. Using the above results, we show that the apparent exponent is determined by the heterodimerisation probability. Our preliminary results are consistent with empirical data for a wide range of organisms, and we believe that through implementing some of the suggested modifications, the model could be well-suited to other types of biological and non-biological networks. 
Nadia Farid and Kim Christensen