We extend the popular force-directed approach to network (or graph) layout to allow separation constraints, which enforce a minimum horizontal or vertical separation between selected pairs of nodes. This simple class of linear constraints is expressive enough to satisfy a wide variety of application-specific layout requirements, including: layout of directed graphs to better show flow; layout with non-overlapping node labels; and layout of graphs with grouped nodes (called clusters). In the stress majorization force-directed layout process, separation constraints can be treated as a quadratic programming problem. We give an incremental algorithm based on gradient projection for efficiently solving this problem. The algorithm is considerably faster than using generic constraint optimization techniques and is comparable in speed to unconstrained stress majorization. We demonstrate the utility of our technique with sample data from a number of practical applications including gene-activation networks, terrorist networks and visualization of high-dimensional data.

Abstract. We present Adaptive Pointing, a novel approach to addressing the common problem of accuracy when using absolute pointing devices for distant interaction. First, we discuss extensively some related work concerning the problem-domain of pointing accuracy when using absolute or relative pointing devices. As a result, we introduce a novel classification scheme to more clearly discriminate between different approaches. Second, the Adaptive Pointing technique is presented and described in detail. The intention behind this approach is to improve pointing performance for absolute input devices by implicitly adapting the Control-Display gain to the current user's needs without violating users' mental model of absolute-device operation. Third, we present an experiment comparing Adaptive Pointing with pure absolute pointing using a laser-pointer as an example of an absolute device. The results show that Adaptive Pointing results in a significant improvement compared with absolute pointing in terms of movement time (19%), error rate (63%), and user satisfaction.

While zoomable user interfaces can improve the usability of applications by easing data access, a drawback is that some users tend to become lost after they have zoomed in. Previous studies indicate that this effect could be related to individual differences in spatial ability. To overcome such orientation problems, many desktop applications feature an additional overview window showing a miniature of the entire information space. Small devices, however, have a very limited screen real estate and incorporating an overview window often means pruning the size of the detail view considerably. Given this context, we report the results of a user study in which 24 participants solved search tasks by using two zoomable scatterplot applications on a PDA -one of the applications featured an overview, the other relied solely on the detail view. In contrast to similar studies for desktop applications, there was no significant difference in user preference between the interfaces. On the other hand, participants solved search tasks faster without the overview. This indicates that, on small screens, a larger detail view can outweigh the benefits gained from an overview window. Individual differences in spatial ability did not have a significant effect on task-completion times although results suggest that participants with higher spatial ability were slowed down by the overview more than low spatial-ability users.

Recent findings from Embodied Cognition reveal strong effects of arm and hand movement on spatial memory. This suggests that input devices may have a far greater influence on users' cognition and users' ability to master a system than we typically believeespecially for spatial panning or zooming & panning user interfaces. We conducted two experiments to observe whether multi-touch instead of mouse input improves users' spatial memory and navigation performance for such UIs. We observed increased performances for panning UIs but not for zooming & panning UIs. We present our results, provide initial explanations and discuss opportunities and pitfalls for interaction designers.

Application programming interfaces (APIs) are the interfaces to existing code structures, such as widgets, frameworks, or toolkits. Therefore, they very much do have an impact on the quality of the resulting system. So, ensuring that developers can make the most out of them is an important challenge. However standard usability evaluation methods as known from HCI have limitations in grasping the interaction between developer and API as most IDEs (essentially the GUI) capture only part of it. In this paper we present the Concept Map method to study the usability of an API over time. This allows us to elicit the mental model of a programmer when using an API and thereby identify usability issues and learning barriers and their development over time.