  While a plethora of hypertext links exist on the Web, only a small amount of
them are regularly clicked. Starting from this observation, we set out to study
large-scale click data from Wikipedia in order to understand what makes a link
successful. We systematically analyze effects of link properties on the
popularity of links. By utilizing mixed-effects hurdle models supplemented with
descriptive insights, we find evidence of user preference towards links leading
to the periphery of the network, towards links leading to semantically similar
articles, and towards links in the top and left-side of the screen. We
integrate these findings as Bayesian priors into a navigational Markov chain
model and by doing so successfully improve the model fits. We further adapt and
improve the well-known classic PageRank algorithm that assumes random
navigation by accounting for observed navigational preferences of users in a
weighted variation. This work facilitates understanding navigational click
behavior and thus can contribute to improving link structures and algorithms
utilizing these structures.
