Steering the Random Surfer on Directed Webgraphs

Ever since the inception of the Web website administrators have tried to steer user browsing behavior for a variety of reasons. For example, to be able to provide the most relevant information, for offering specific products, or to increase revenue from advertisements. One common approach to steer or bias the browsing behavior of users is to influence the link selection process by, for example, highlighting or repositioning links on a website. In this paper, we present a methodology for (i) expressing such navigational biases based on the random surfer model, and for (ii) measuring the consequences of the implemented biases. By adopting a model-based approach we are able to perform a wide range of experiments on seven empirical datasets. Our analyses allows us to gain novel insights into the consequences of navigational biases. Further, we unveil that navigational biases may have significant effects on the browsing processes of users and their typical whereabouts on a website. The first contribution of our work is the formalization of an approach to analyze consequences of navigational biases on the browsing dynamics and visit probabilities of specific pages of a website. Second, we apply this approach to analyze several empirical datasets and improve our understanding of the effects of different biases on real-world websites. In particular, we find that on webgraphs - contrary to undirected networks - typical biases always increase the certainty of the random surfer when selecting a link. Further, we observe significant side effects of biases, which indicate that for practical settings website administrators might need to carefully balance the desired outcomes against undesirable side effects.