Can animation support the visualisation of dynamic graphs?

Animation and small multiples are methods for visualizing dynamically evolving graphs. Animations present an interactive movie of the data where positions of nodes are smoothly interpolated as the graph evolves. Nodes fade in/out as they are added/removed from the data set. Small multiples presents the data like a comic book with the graph at various states in separate windows. The user scans these windows to see how the data evolves. In a recent experiment, drawing stability (known more widely as the “mental map”) was shown to help users follow specific nodes or long paths in dynamically evolving data. However, no significant difference between animation and small multiples presentations was found. In this paper, we look at data where the nodes in the graph have low drawing stability and analyze it with new error metrics: measuring how close the given answer is from the correct answer on a continuous scale. We find evidence that when the stability of the drawing is low and important nodes in the task cannot be highlighted throughout the time series, animation can improve task performance when compared to the use of small multiples

On the effective visualisation of dynamic attribute cascades

Cascades appear in many applications, including biological graphs and social media analysis. In a cascade, a dynamic attribute propagates through a graph, following its edges. We present the results of a formal user study that tests the effectiveness of different types of cascade visualisations on node-link diagrams for the task of judging cascade spread. Overall, we found that a small multiples presentation was significantly faster than animation with no significant difference in terms of error rate. Participants generally preferred animation over small multiples and a hierarchical layout to a force-directed layout. Considering each presentation method separately, when comparing force-directed layouts to hierarchical layouts, hierarchical layouts were found to be significantly faster for both presentation methods and significantly more accurate for animation. Representing the history of the cascade had no significant effect. Thus, for our task, this experiment supports the use of a small multiples interface with hierarchically drawn graphs for the visualisation of cascades. This work is important because without these empirical results, designers of dynamic multivariate visualisations (in many applications) would base their design decisions on intuition with little empirical support as to whether these decisions enhance usability

Extending adjacency matrices to 3D with triangles

Social networks are the fabric of society and the subject of frequent visual analysis. Closed triads represent triangular relationships between three people in a social network and are significant for understanding inherent interconnections and influence within the network. The most common methods for representing social networks (node-link diagrams and adjacency matrices) are not optimal for understanding triangles. We propose extending the adjacency matrix form to 3D for better visualization of network triads. We design a 3D matrix reordering technique and implement an immersive interactive system to assist in visualizing and analyzing closed triads in social networks. A user study and usage scenarios demonstrate that our method provides substantial added value over node-link diagrams in improving the efficiency and accuracy of manipulating and understanding the social network triads.Comment: 10 pages, 8 figures and 3 table

A Classification of Infographics

Classifications are useful for describing existing phenomena and guiding further investigation. Several classifications of diagrams have been proposed, typically based on analytical rather than empirical methodologies. A notable exception is the work of Lohse and his colleagues, published in Communications of the ACM in December 1994. The classification of diagrams that Lohse proposed was derived from bottom-up grouping data collected from sixteen participants and based on 60 diagrams. Mean values on ten Likert-scales were used to predict diagram class. We follow a similar methodology to Lohse, using real-world infographics (i.e. embellished data charts) as our stimuli. We propose a structural classification of infographics, and determine whether infographics class can be predicted from values on Likert scales

Breakout group allocation schedules and the social golfer problem with adjacent group sizes

The current pandemic has led schools and universities to turn to online meeting software solutions such as Zoom and Microsoft Teams. The teaching experience can be enhanced via the use of breakout rooms for small group interaction. Over the course of a class (or over several classes), the class will be allocated to breakout groups multiple times over several rounds. It is desirable to mix the groups as much as possible, the ideal being that no two students appear in the same group in more than one round. In this paper, we discuss how the problem of scheduling balanced allocations of students to sequential breakout rooms directly corresponds to a novel variation of a well-known problem in combinatorics (the social golfer problem), which we call the social golfer problem with adjacent group sizes. We explain how solutions to this problem can be obtained using constructions from combinatorial design theory and how they can be used to obtain good, balanced breakout room allocation schedules. We present our solutions for up to 50 students and introduce an online resource that educators can access to immediately generate suitable allocation schedules

The state of the art in empirical user evaluation of graph visualizations

While graph drawing focuses more on the aesthetic representation of node-link diagrams, graph visualization takes into account other visual metaphors making them useful for graph exploration tasks in information visualization and visual analytics. Although there are aesthetic graph drawing criteria that describe how a graph should be presented to make it faster and more reliably explorable, many controlled and uncontrolled empirical user studies flourished over the past years. The goal of them is to uncover how well the human user performs graph-specific tasks, in many cases compared to previously designed graph visualizations. Due to the fact that many parameters in a graph dataset as well as the visual representation of them might be varied and many user studies have been conducted in this space, a state-of-the-art survey is needed to understand evaluation results and findings to inform the future design, research, and application of graph visualizations. In this paper, we classify the present literature on the topmost level into graph interpretation, graph memorability, and graph creation where the users with their tasks stand in focus of the evaluation not the computational aspects. As another outcome of this work, we identify the white spots in this field and sketch ideas for future research directions

Peer Assessment: Encouraging Reflection on Interface Design

Peer assessment is recognised as a useful learning activity, not merely as a means by which assignments can be marked. In a design subject such as human-computer interaction, peer-assessment offers a unique opportunity for students to be exposed to a wide range of different designs in an environment that ensures that they reflect on these designs. However, it is important that the marking criteria are well specified and unambiguous. This paper reports on the use of peer-assessment as an appropriate learning activity for revealing the wide range of design issues and dimensions in interface design, where formal marking is based on clear, functional criteria. Subjective consideration of the interfaces is encouraged through a ranking system and the subsequent elicitation of interface design principles. The successful implementation and acceptance of this scheme demonstrates its benefits both as a learning activity and as an opportunity for student reflection

Sketched graph drawing: a lesson in empirical studies

This paper reports on a series of three similar graph drawing empirical studies, and describes the results of investigating subtle variations on the experimental method. Its purpose is two-fold: to report the results of the experiments, as well as to illustrate how easy it is to inadvertently make conclusions that may not stand up to scrutiny. While the results of the initial experiment were validated, instances of speculative conclusions and inherent bias were identified. This research highlights the importance of stating the limitations of any experiment, being clear about conclusions that are speculative, and not assuming that (even minor) experimental decisions will not affect the results

Twelve years of diagrams research

Research into the use of diagrams is an interdisciplinary endeavour, encompassing disciplines as diverse as psychology, architecture and artificial intelligence. It is also a relatively new research area, with the first meeting of like-minded researchers interested in studying diagrams taking place in 1997. Now that diagrams research is more established, it is timely to review its scope, nature and progress. This paper reviews diagrams research over the past twelve years, as represented in the proceedings of the International Conference on the Theory and Application of Diagrams. In summarising the contents of these proceedings, a taxonomy describing the scope of diagrams research is proposed, the several research issues covered are identified, and the extent to which layout and aesthetics form part of this body of research is discussed. In concluding, trends and under-represented areas are noted and discussed. The aim of the paper is not only to summarise the research covered in this particular conference, but to provide a basis for on-going discussion on the changing nature of diagrams research