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.
Based on this wide range of studies aiming at understanding human Web navigation, a series of navigational regularities, patterns and strategies have been suggested. For example, West and Leskovec @cite_32 found trade-offs between similarity and degree in navigational behavior suggesting different phases in user sessions, namely an exploration (orientation) and an exploitation (goal-seeking) phase @cite_14 . Subsequent research has suggested that humans prefer to navigate between semantically similar websites @cite_43 @cite_27 @cite_7 , have preferences for choosing links at the beginning of pages @cite_39 @cite_26 @cite_4 , and that navigational patterns exhibit regularities with respect to underlying network characteristics @cite_43 @cite_26 @cite_7 @cite_39 .
@cite_14 @cite_26 @cite_4 @cite_7 @cite_32 @cite_39 @cite_43 @cite_27
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In this work we study how people navigate the information network of Wikipedia and investigate i free-form navigation by studying all clicks within the English Wikipedia over an entire month and ii goal-directed Wikipedia navigation by analyzing wikigames, where users are challenged to retrieve articles by following links. To study how the organization of Wikipedia articles in terms of layout and links affects navigation behavior, we first investigate the characteristics of the structural organization and of hyperlinks in Wikipedia and then evaluate link selection models based on article structure and other potential influences in navigation, such as the generality of an article's topic. In free-form Wikipedia navigation, covering all Wikipedia usage scenarios, we find that click choices can be best modeled by a bias towards article structure, such as a tendency to click links located in the lead section. For the goal-directed navigation of wikigames, our findings confirm the zoom-out and the homing-in phases identified by previous work, where users are guided by generality at first and textual similarity to the target later. However, our interpretation of the link selection models accentuates that article structure is the best explanation for the navigation paths in all except these initial and final stages. Overall, we find evidence that users more frequently click on links that are located close to the top of an article. The structure of Wikipedia articles, which places links to more general concepts near the top, supports navigation by allowing users to quickly find the better-connected articles that facilitate navigation. Our results highlight the importance of article structure and link position in Wikipedia navigation and suggest that better organization of information can help make information networks more navigable. People regularly face tasks that can be understood as navigation in information networks, where the goal is to find a path between two given nodes. In many such situations, the navigator only gets local access to the node currently under inspection and its immediate neighbors. This lack of global information about the network notwithstanding, humans tend to be good at finding short paths, despite the fact that realworld networks are typically very large. One potential reason for this could be that humans possess vast amounts of background knowledge about the world, which they leverage to make good guesses about possible solutions. In this paper we ask the question: Are human-like high-level reasoning skills really necessary for finding short paths? To answer this question, we design a number of navigation agents without such skills, which use only simple numerical features. We evaluate the agents on the task of navigating Wikipedia, a domain for which we also possess large-scale human navigation data. We observe that the agents find shorter paths than humans on average and therefore conclude that, perhaps surprisingly, no sophisticated background knowledge or high-level reasoning is required for navigating the complex Wikipedia network. Recent literature has presented evidence that the study of navigation in complex networks is useful to understand their dynamics and topology. Two main approaches are usually considered: navigation of random walkers and navigation of directed walkers. Unlike these approaches ours supposes that a traveler walks optimally in order to minimize the cost of the walking. If this happens, two extreme regimes arise—one dominated by directed walkers and the other by random walkers. We try to characterize the critical point of the transition from one regime to the other in function of the connectivity and the size of the network. Furthermore, we show that this approach can be used to generalize several concepts presented in the literature concerning random navigation and direct navigation. Finally, we defend that investigating the extreme regimes dominated by random walkers and directed walkers is not sufficient to correctly assess the characteristics of navigation in complex 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.