document: Human wayfinding in information networks Navigating information spaces is an essential part of our everyday lives , and in order to design efficient and user-friendly information systems , it is important to understand how humans navigate and find the information they are looking for . We perform a large-scale study of human wayfinding , in which , given a network of links between the concepts of Wikipedia , people play a game of finding a short path from a given start to a given target concept by following hyperlinks . What distinguishes our setup from other studies of human Web-browsing behavior is that in our case people navigate a graph of connections between concepts , and that the exact goal of the navigation is known ahead of time . We study more than 30,000 goal-directed human search paths and identify strategies people use when navigating information spaces . We find that human wayfinding , while mostly very efficient , differs from shortest paths in characteristic ways . Most subjects navigate through high-degree hubs in the early phase , while their search is guided by content features thereafter . We also observe a trade-off between simplicity and efficiency : conceptually simple solutions are more common but tend to be less efficient than more complex ones . Finally , we consider the task of predicting the target a user is trying to reach . We design a model and an efficient learning algorithm . Such predictive models of human wayfinding can be applied in intelligent browsing interfaces .
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