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Analysis of aggregate Web traffic has shown that PageRank is a poor model ofhow people actually navigate the Web. Using the empirical traffic patternsgenerated by a thousand users over the course of two months, we characterizethe properties of Web traffic that cannot be reproduced by Markovian models, inwhich destinations are independent of past decisions. In particular, we showthat the diversity of sites visited by individual users is smaller and morebroadly distributed than predicted by the PageRank model; that link traffic ismore broadly distributed than predicted; and that the time between consecutivevisits to the same site by a user is less broadly distributed than predicted.To account for these discrepancies, we introduce a more realistic navigationmodel in which agents maintain individual lists of bookmarks that are used asteleportation targets. The model can also account for branching, a trafficproperty caused by browser features such as tabs and the back button. The modelreproduces aggregate traffic patterns such as site popularity, while alsogenerating more accurate predictions of diversity, link traffic, and returntime distributions. This model for the first time allows us to capture theextreme heterogeneity of aggregate traffic measurements while explaining themore narrowly focused browsing patterns of individual users.

Remembering what we like: Toward an agent-based model of Web traffic