A parent-centered radial layout algorithm for interactive graph visualization and animation

We have developed (1) a graph visualization system that allows users to explore graphs by viewing them as a succession of spanning trees selected interactively, (2) a radial graph layout algorithm, and (3) an animation algorithm that generates meaningful visualizations and smooth transitions between graphs while minimizing edge crossings during transitions and in static layouts. Our system is similar to the radial layout system of Yee et al. (2001), but differs primarily in that each node is positioned on a coordinate system centered on its own parent rather than on a single coordinate system for all nodes. Our system is thus easy to define recursively and lends itself to parallelization. It also guarantees that layouts have many nice properties, such as: it guarantees certain edges never cross during an animation. We compared the layouts and transitions produced by our algorithms to those produced by Yee et al. Results from several experiments indicate that our system produces fewer edge crossings during transitions between graph drawings, and that the transitions more often involve changes in local scaling rather than structure. These findings suggest the system has promise as an interactive graph exploration tool in a variety of settings

Understanding Visualization: A formal approach using category theory and semiotics

This article combines the vocabulary of semiotics and category theory to provide a formal analysis of visualization. It shows how familiar processes of visualization fit the semiotic frameworks of both Saussure and Peirce, and extends these structures using the tools of category theory to provide a general framework for understanding visualization in practice, including: relationships between systems, data collected from those systems, renderings of those data in the form of representations, the reading of those representations to create visualizations, and the use of those visualizations to create knowledge and understanding of the system under inspection. The resulting framework is validated by demonstrating how familiar information visualization concepts (such as literalness, sensitivity, redundancy, ambiguity, generalizability, and chart junk) arise naturally from it and can be defined formally and precisely. This article generalizes previous work on the formal characterization of visualization by, inter alia, Ziemkiewicz and Kosara and allows us to formally distinguish properties of the visualization process that previous work does not

Deriving a holistic cognitive fit model for an optimal visualization of data for management decisions

Research shows that managerial decision making is directly correlated to both, the swift availability, and subsequently the ease of interpretation of the relevant information. Visualizations are already widely used to transform raw data into a more understandable format and to compress the constantly growing amount of information produced. However, research in this area is highly fragmented and results are contradicting. This paper proposes a preliminary model based on an extensive literature review including top current research on cognition theory. Furthermore an early stage validation of this model by experimental research using structural equation modeling is presented. The authors are able to identify task complexity as one of the most important predicting variables for information perception of visual data, however, other influences are significant as well (data density, domain expertise, working memory capacity and subjective visual complexity

Improving Information Perception of Graphical Displays – an Experimental Study on the Display of Column Graphs

Due to the fact that the quality of decisions is linked to the availability of information and to the ability of the human brain to process this in an effective and efficient way, its selection and representation are of major importance in business communication. Graphs and tables are widely used to transform raw data into a more understandable format, but there are not any empirically tested guidelines that consider the cognition and perception abilities of humans. This paper therefore explores how specific visual designs applied to column graphs influence effectiveness and efficiency by applying the technique of eye-tracking to make an accurate assessment of what the observer is looking at. The tested design elements show significant results and allow the deduction of the following design guidelines for column graphs: do not use a 3D view for depicting two dimensional data, do not use non-zero or broken axes, do show label values, do not use horizontal gridlines or the label axis when showing label values and do align the label values depending on the available space (either horizontally or vertically)

A user study on curved edges in graph visualization

Recently there has been increasing research interest in displaying graphs with curved edges to produce more readable visualizations. While there are several automatic techniques, little has been done to evaluate their effectiveness empirically. In this paper we present two experiments studying the impact of edge curvature on graph readability. The goal is to understand the advantages and disadvantages of using curved edges for common graph tasks compared to straight line segments, which are the conventional choice for showing edges in node-link diagrams. We included several edge variations: straight edges, edges with different curvature levels, and mixed straight and curved edges. During the experiments, participants were asked to complete network tasks including determination of connectivity, shortest path, node degree, and common neighbors. We also asked the participants to provide subjective ratings of the aesthetics of different edge types. The results show significant performance differences between the straight and curved edges and clear distinctions between variations of curved edges

How to Display Group Information on Node-Link Diagrams: An Evaluation

We present the results of evaluating four techniques for displaying group or cluster information overlaid on node-link diagrams: node coloring, GMap, BubbleSets, and LineSets. The contributions of the paper are three fold. First, we present quantitative results and statistical analyses of data from an online study in which approximately 800 subjects performed 10 types of group and network tasks in the four evaluated visualizations. Specifically, we show that BubbleSets is the best alternative for tasks involving group membership assessment; that visually encoding group information over basic node-link diagrams incurs an accuracy penalty of about 25 percent in solving network tasks; and that GMap's use of prominent group labels improves memorability. We also show that GMap's visual metaphor can be slightly altered to outperform BubbleSets in group membership assessment. Second, we discuss visual characteristics that can explain the observed quantitative differences in the four visualizations and suggest design recommendations. This discussion is supported by a small scale eye-tracking study and previous results from the visualization literature. Third, we present an easily extensible user study methodology

Analyzing Eye-Tracking Information in Visualization and Data Space: from Where on the Screen to What on the Screen.

Eye-tracking data is currently analyzed in the image space that gaze-coordinates were recorded in, generally with the help of overlays such as heatmaps or scanpaths, or with the help of manually defined areas of interest (AOI). Such analyses, which focus predominantly on where on the screen users are looking, require significant manual input and are not feasible for studies involving many subjects, long sessions, and heavily interactive visual stimuli. Alternatively, we show that it is feasible to collect and analyze eye-tracking information in data space. Specifically, the visual layout of visualizations with open source code that can be instrumented is known at rendering time, and thus can be used to relate gaze-coordinates to visualization and data objects that users view, in real time. We demonstrate the effectiveness of this approach by showing that data collected using this methodology from nine users working with an interactive visualization, was well aligned with the tasks that those users were asked to solve, and similar to annotation data produced by five human coders. Moreover, we introduce an algorithm that, given our instrumented visualization, could translate gaze-coordinates into viewed objects with greater accuracy than simply binning gazes into dynamically defined AOIs. Finally, we discuss the challenges, opportunities, and benefits of analyzing eye-tracking in visualization and data space

Interactive, tree-based graph visualization

We introduce an interactive graph visualization scheme that allows users to explore graphs by viewing them as a sequence of spanning trees, rather than the entire graph all at once. The user determines which spanning trees are displayed by selecting a vertex from the graph to be the root. Our main contributions are a graph drawing algorithm that generates meaningful representations of graphs using extracted spanning trees, and a graph animation algorithm for creating smooth, continuous transitions between graph drawings. We conduct experiments to measure how well our algorithms visualize graphs and compare them to another visualization scheme