Recently several authors have proposed stochastic evolutionary models for the growth of the Web graph and other networks that give rise to power-law distributions. These models are based on the notion of preferential attachment, leading to the "rich get richer" phenomenon. We present a generalization of the basic model by allowing deletion of individual links and show that it also gives rise to a power-law distribution. We derive the mean-field equations for this stochastic model and show that, by examining a snapshot of the distribution at the steady state of the model, we are able to determine the extent to which link deletion has taken place and estimate the probability of deleting a link. Applying our model to actual Web graph data provides evidence of the extent to which link deletion has occurred. We also discuss a problem that frequently arises in estimating the power-law exponent from empirical data and a few possible methods for dealing with this, indicating our preferred approach. Using this approach our analysis of the data suggests a powerlaw exponent of approximately 2.15 for the distribution of inlinks in the Web graph, rather than the widely published value of 2.1.
Similarly, Fenner et al. REF contributed a theoretical model for generalizing the rich-get-richer model of network evolution, which focuses mainly on growth dynamics, by extending it to link deletion in the Web network.
A stochastic model for the evolution of the Web allowing link deletion
TOIT
Physics Computer Science