Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Communications of the ACM
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
ACM Transactions on Internet Technology (TOIT)
Random Structures & Algorithms
Statistical mechanics of complex networks
Statistical mechanics of complex networks
A stochastic evolutionary growth model for social networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
A method for measuring the evolution of a topic on the Web: The case of “informetrics”
Journal of the American Society for Information Science and Technology
Arrival and departure dynamics in social networks
Proceedings of the sixth ACM international conference on Web search and data mining
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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 power-law exponent of approximately 2.15 for the distribution of inlinks in the Web graph, rather than the widely published value of 2.1.