SearcharxivSearch

arXiv subjects

Nadia Farid

Publications and source records attributed to Nadia Farid.

2 recordsLinked to original sources

On the scaling of probability density functions with apparent power-law exponents less than unity

We derive general properties of the finite-size scaling of probability density functions and show that when the apparent exponent \tautilde of a probability density is less than 1, the associated finite-size scaling ansatz has a scaling exponent τequal to 1, provided that the fraction of events in the universal scaling part of the probability density function is non-vanishing in the thermodynamic limit. We find the general result that τ>=1 and τ>=\tautilde. Moreover, we show that if the scaling function G(x) approaches a non-zero constant for small arguments, \lim_{x-> 0} G(x) > 0, then τ=\tautilde. However, if the scaling function vanishes for small arguments, \lim_{x-> 0} G(x) = 0, then τ=1, again assuming a non-vanishing fraction of universal events. Finally, we apply the formalism developed to examples from the literature, including some where misunderstandings of the theory of scaling have led to erroneous conclusions.

cond-mat.stat-mech

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.

physics.bio-ph