Stochastic models for tabbed browsing

We present a model of tabbed browsing that represents a hybrid between a Markov process capturing the graph of hyperlinks, and a branching process capturing the birth and death of tabs. We present a mathematical criterion to characterize whether the process has a steady state independent of initial conditions, and we show how to characterize the limiting behavior in both cases. We perform a series of experiments to compare our tabbed browsing model with pagerank, and show that tabbed browsing is able to explain 15-25% of the deviation between actual measured browsing behavior and the behavior predicted by the simple pagerank model. We find this to be a surprising result, as the tabbed browsing model does not make use of any notion of site popularity, but simply captures deviations in user likelihood to open and close tabs from a particular node in the graph. © 2010 International World Wide Web Conference Committee (IW3C2).