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. Models of human navigation play an important role for understanding and facilitating user behavior in hypertext systems. In this paper, we conduct a series of principled experiments with decentralized search - an established model of human navigation in social networks - and study its applicability to information networks. We apply several variations of decentralized search to model human navigation in information networks and we evaluate the outcome in a series of experiments. In these experiments, we study the validity of decentralized search by comparing it with human navigational paths from an actual information network - Wikipedia. We find that (i) navigation in social networks appears to differ from human navigation in information networks in interesting ways and (ii) in order to apply decentralized search to information networks, stochastic adaptations are required. Our work illuminates a way towards using decentralized search as a valid model for human navigation in information networks in future work. Our results are relevant for scientists who are interested in modeling human behavior in information networks and for engineers who are interested in using models and simulations of human behavior to improve on structural or user interface aspects of hypertextual systems. One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work. When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web. An important part of finding information online involves clicking from page to page until an information need is fully satisfied. This is a complex task that can easily be frustrating and force users to give up prematurely. An empirical analysis of what makes users abandon click-based navigation tasks is hard, since most passively collected browsing logs do not specify the exact target page that a user was trying to reach. We propose to overcome this problem by using data collected via Wikispeedia, a Wikipedia-based human-computation game, in which users are asked to navigate from a start page to an explicitly given target page (both Wikipedia articles) by only tracing hyperlinks between Wikipedia articles. Our contributions are two-fold. First, by analyzing the differences between successful and abandoned navigation paths, we aim to understand what types of behavior are indicative of users giving up their navigation task. We also investigate how users make use of back clicks during their navigation. We find that users prefer backtracking to high-degree nodes that serve as landmarks and hubs for exploring the network of pages. Second, based on our analysis, we build statistical models for predicting whether a user will finish or abandon a navigation task, and if the next action will be a back click. Being able to predict these events is important as it can potentially help us design more human-friendly browsing interfaces and retain users who would otherwise have given up navigating a website.