Abstract-The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily resolve the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this problem, we present NodeTrix, a hybrid representation for networks that combines the advantages of two traditional representations: node-link diagrams are used to show the global structure of a network, while arbitrary portions of the network can be shown as adjacency matrices to better support the analysis of communities. A key contribution is a set of interaction techniques. These allow analysts to create a NodeTrix visualization by dragging selections to and from node-link and matrix forms, and to flexibly manipulate the NodeTrix representation to explore the dataset and create meaningful summary visualizations of their findings. Finally, we present a case study applying NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate the capabilities of NodeTrix as both an exploration tool and an effective means of communicating results.

Summary: NAViGaTOR is a powerful graphing application for the 2D and 3D visualization of biological networks. NAViGaTOR includes a rich suite of visual mark-up tools for manual and automated annotation, fast and scalable layout algorithms and OpenGL hardware acceleration to facilitate the visualization of large graphs. Publication-quality images can be rendered through SVG graphics export. NAViGaTOR supports community-developed data formats (PSI-XML, BioPax and GML), is platform-independent and is extensible through a plug-in architecture.Availability: NAViGaTOR is freely available to the research community from http://ophid.utoronto.ca/navigator/. Installers and documentation are provided for 32- and 64-bit Windows, Mac, Linux and Unix.Contact: juris@ai.utoronto.caSupplementary information: Supplementary data are available at Bioinformatics online.

There exist several user interface widgets that dynamically grow in size in response to the user's focus of attention. Some of these, such as icons in toolbars, expand to facilitate their selection − allowing for a reduced initial size in an attempt to optimize screen space use. However, selection performance may be degraded by this decreased initial widget size. We describe an experiment which explores the effects of varying parameters of expansion techniques in a selection task. Our results suggest that Fitts' law can model and predict performance in such tasks. They also indicate that performance is governed by the target's final size, not its initial one. Further, performance is dependent on the target's final size even when the target only begins expanding as late as after 90% of the movement towards the target has already been completed. These results indicate that expanding widgets can be used without sacrificing performance.

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A standard approach for visualizing multivariate networks is to use one or more multidimensional views (for example, scatterplots) for selecting nodes by various metrics, possibly coordinated with a node-link view of the network. In this paper, we present three novel approaches for achieving a tighter integration of these views through hybrid techniques for multidimensional visualization, graph selection and layout. First, we present the FlowVizMenu, a radial menu containing a scatterplot that can be popped up transiently and manipulated with rapid, fluid gestures to select and modify the axes of its scatterplot. Second, the FlowVizMenu can be used to steer an attribute-driven layout of the network, causing certain nodes of a node-link diagram to move toward their corresponding positions in a scatterplot while others can be positioned manually or by force-directed layout. Third, we describe a novel hybrid approach that combines a scatterplot matrix (SPLOM) and parallel coordinates called the Parallel Scatterplot Matrix (P-SPLOM), which can be used to visualize and select features within the network. We also describe a novel arrangement of scatterplots called the Scatterplot Staircase (SPLOS) that requires less space than a traditional scatterplot matrix. Initial user feedback is reported.

Fig. 1. Given a dynamic graph defined over a series of time slices, our DiffAni hybrid visualization displays it as a sequence of consecutive tiles. Each tile may show one or more time slices, and there are three kinds of tiles: diff tiles (indicated with a solid border and two time slice numbers below them, e.g., the left-most tile), animation tiles (indicated with a dashed border and two time slice numbers below them, e.g., the tile covering time slices 2 to 3), and small multiple tiles (indicated with a solid border and a single time slice number below them, e.g., the right-most tile). Nodes and edges are colored in red if they are being removed, or green if they are being added, over time slices. The radial menu above is being used to convert a diff tile into two small multiple ("SM") tiles.Abstract-Visualization of dynamically changing networks (graphs) is a significant challenge for researchers. Previous work has experimentally compared animation, small multiples, and other techniques, and found trade-offs between these. One potential way to avoid such trade-offs is to combine previous techniques in a hybrid visualization. We present two taxonomies of visualizations of dynamic graphs: one of non-hybrid techniques, and one of hybrid techniques. We also describe a prototype, called DiffAni, that allows a graph to be visualized as a sequence of three kinds of tiles: diff tiles that show difference maps over some time interval, animation tiles that show the evolution of the graph over some time interval, and small multiple tiles that show the graph state at an individual time slice. This sequence of tiles is ordered by time and covers all time slices in the data. An experimental evaluation of DiffAni shows that our hybrid approach has advantages over non-hybrid techniques in certain cases.

In Augmented Reality (AR), users perceive virtual content anchored in the real world. It is used in medicine, education, games, navigation, maintenance, product design, and visualization, in both single-user and multi-user scenarios. Multi-user AR has received limited attention from researchers, even though AR has been in development for more than two decades. We present the state of existing work at the intersection of AR and Computer-Supported Collaborative Work (AR-CSCW), by combining a systematic survey approach with an exploratory, opportunistic literature search. We categorize 65 papers along the dimensions of space, time, role symmetry (whether the roles of users are symmetric), technology symmetry (whether the hardware platforms of users are symmetric), and output and input modalities. We derive design considerations for collaborative AR environments, and identify under-explored research topics. These include the use of heterogeneous hardware considerations and 3D data exploration research areas. This survey is useful for newcomers to the field, readers interested in an overview of CSCW in AR applications, and domain experts seeking up-to-date information.

GPS, RFID, and other technologies have made it increasingly common to track the positions of people and objects over time as they move through two-dimensional spaces. Visualizing such spatio-temporal movement data is challenging because each person or object involves three variables (two spatial variables as a function of the time variable), and simply plotting the data on a 2D geographic map can result in overplotting and occlusion that hides details. This also makes it difficult to understand correlations between space and time. Software such as GeoTime can display such data with a three-dimensional visualization, where the 3rd dimension is used for time. This allows for the disambiguation of spatially overlapping trajectories, and in theory, should make the data clearer. However, previous experimental comparisons of 2D and 3D visualizations have so far found little advantage in 3D visualizations, possibly due to the increased complexity of navigating and understanding a 3D view. We present a new controlled experimental comparison of 2D and 3D visualizations, involving commonly performed tasks that have not been tested before, and find advantages in 3D visualizations for more complex tasks. In particular, we tease out the effects of various basic interactions and find that the 2D view relies significantly on "scrubbing" the timeline, whereas the 3D view relies mainly on 3D camera navigation. Our work helps to improve understanding of 2D and 3D visualizations of spatio-temporal data, particularly with respect to interactivity.