Evaluation of two interaction techniques for visualization of dynamic graphs

Several techniques for visualization of dynamic graphs are based on different spatial arrangements of a temporal sequence of node-link diagrams. Many studies in the literature have investigated the importance of maintaining the user's mental map across this temporal sequence, but usually each layout is considered as a static graph drawing and the effect of user interaction is disregarded. We conducted a task-based controlled experiment to assess the effectiveness of two basic interaction techniques: the adjustment of the layout stability and the highlighting of adjacent nodes and edges. We found that generally both interaction techniques increase accuracy, sometimes at the cost of longer completion times, and that the highlighting outclasses the stability adjustment for many tasks except the most complex ones.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

Fast filtering and animation of large dynamic networks

Detecting and visualizing what are the most relevant changes in an evolving network is an open challenge in several domains. We present a fast algorithm that filters subsets of the strongest nodes and edges representing an evolving weighted graph and visualize it by either creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is an approximation of exponential sliding time-window that scales linearly with the number of interactions. We compare the algorithm against rectangular and exponential sliding time-window methods. Our network filtering algorithm: i) captures persistent trends in the structure of dynamic weighted networks, ii) smoothens transitions between the snapshots of dynamic network, and iii) uses limited memory and processor time. The algorithm is publicly available as open-source software.Comment: 6 figures, 2 table

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

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

A Review of Temporal Data Visualizations Based on Space-Time Cube Operations

International audienceWe review a range of temporal data visualization techniques through a new lens, by describing them as series of op- erations performed on a conceptual space-time cube. These operations include extracting subparts of a space-time cube, flattening it across space or time, or transforming the cube's geometry or content. We introduce a taxonomy of elementary space-time cube operations, and explain how they can be combined to turn a three-dimensional space-time cube into an easily-readable two-dimensional visualization. Our model captures most visualizations showing two or more data dimensions in addition to time, such as geotemporal visualizations, dynamic networks, time-evolving scatterplots, or videos. We finally review interactive systems that support a range of operations. By introducing this conceptual framework we hope to facilitate the description, criticism and comparison of existing temporal data visualizations, as well as encourage the exploration of new techniques and systems

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

Development of Visualization Tools for Dynamic Networks and Evaluation of Visual Stability Characteristics

Das dynamische Graphenzeichnen ist das Mittel der Wahl, wenn es um die Analyse und Visualisierung dynamischer Netzwerke geht. Diese Zeichnungen werden oft durch Festhalten aufeinanderfolgender Datenreihen oder “Snapshots” des untersuchten Netzwerkes erzeugt. Für jede von diesen Zeichnungen wird mit Hilfe des ausgewählten Algorithmus eine unabhängige Graphenzeichnung berechnet und die resultierende Sequenz wird dem Benutzer in einer vorbestimmten Reihenfolge präsentiert. Trotz der Einfachheit dieser Methode tauchen bei der dynamischen Graphenzeichnung mit der vorher genannten Strategie Probleme auf. Teilnehmer, Verbindungen und Muster können während der Untersuchung des dynamischen Netzwerkes ihre Position auf der Darstellung verändern. Außerdem neigen dynamische Graphenzeichnungen dazu, fortlaufend Elemente ohne vorhergehende Information hinzuzufügen und zu entfernen. Als Konsequenz ergibt sich die Schwierigkeit, die Entwicklung der Mitglieder des Netzwerkes zu beobachten. Es wurden verschiedene Techniken zur Anpassung von Layouts entwickelt, welche das Ziel haben, die Änderungen der dynamischen Graphenzeichnung zu minimieren. Einige von ihnen schlagen vor, dass die Grundstruktur der Zeichnung jederzeit beibehalten werden muss. Andere wiederum, dass jeder Teilnehmer und jede Beziehung einer fixen Position im Euklidischen Raum zugeordnet werden soll. Eine neu entwickelte Technik schlägt eine Alternative vor: Mehrere Teilnehmer können gleichzeitig einen Knotenpunkt im Euklidischen Raum beanspruchen, solange sie nicht zum selben Zeitpunkt erscheinen. Mehrere Beziehungen können unter den vorgenannten Bedingungen dementsprechend denselben Eckpunkt im Euklidischen Raum beanspruchen. Daraus folgt, dass die dynamische Graphenzeichnung ihre Veränderungen minimiert bis hin zu einem Zustand, in dem es als "visuell stabil" angesehen werden kann. Diese Arbeit zeigt inwieweit die visuelle Stabilität einer dynamischen Graphenzeichnung die Benutzererfahrung und die Effektivität der visuellen Suche beim Verfolgen der Mitglieder oder Netzwerkeigenschaften beeinflusst. Zu diesem Zweck wurde ein Framework zur Unterstützung flexibler Visualisierungstechniken entwickelt. Es diente als Plattform, um existierende Techniken zu bewerten. Solche Bewertungen kombinieren den Gebrauch von Fragebögen, um Informationen über die Nutzererfahrung zu sammeln, ein Eye-Tracking System, um die Augenbewegungen zu erfassen sowie ein neues mathematisches Modell zur Quantifizierung der visuellen Stabilität einer dynamischen Graphenzeichnung. Die daraus folgenden Resultate ergeben, dass es einen Zielkonflikt zwischen der Benutzererfahrung und der Effizienz der visuellen Suche gibt, welche von der visuellen Stabilität der dynamischen Graphenzeichnung abhängt. Einerseits bieten dynamische Graphenzeichnungen mit einem höheren Niveau an visueller Stabilität eine bessere Benutzererfahrung bei Verfolgungsaufgaben, aber eine schlechtere Effizienz bei der visuellen Suche. Andererseits bieten dynamische Graphenzeichnungen mit einer geringeren visuellen Stabilität eine nicht zufriedenstellende Benutzererfahrung, jedoch im Austausch eine Verbesserung der Effizienz der visuellen Suche. Dieses Ergebnis wird genutzt, um visuell stabile Beschreibungen zu entwickeln, die darauf abzielen, die Netzwerkeigenschaften über einen gewissen Zeitraum zu untersuchen. Solche Beschreibungen und Empfehlungen bedienen sich Merkmalen wie Skalierung und Hervorhebung, um die Effizienz der visuellen Suche zu verbessern.Dynamic graph drawings are the metaphor of choice when it comes to the analysis and visualization of dynamic networks. These drawings are often created by capturing a successive sequence of states or “snapshots” from the network under study. Then, for each one of them, a graph drawing is independently computed with the layout algorithm of preference and the resulting sequence is presented to the user in a predefined order. Despite the simplicity of the method, dynamic graph drawings created with the pre- vious strategy possess some problems. Actors, relations or patterns can change their position on the canvas as the dynamic network is explored. Furthermore, dynamic graph drawings tend to constantly add and remove elements without prior information. As a consequence, it is very difficult to observe how the members of the network evolve over time. The scientific community has developed a series of layout adjustment techniques, which aim at minimizing the changes in a dynamic graph drawing. Some of them suggest that the “shape” of the drawing must be maintained at all time. Others that every actor and relation must be assigned to a fixed position in the Euclidean Space. However, a recently developed technique proposes an alternative. Multiple actors can occupy the same node position in the Euclidean Space, as long as they do not appear at the same point in time. Likewise, multiple relations can occupy the same edge position in the Euclidean Space following the principle aforementioned. As the result, a dynamic graph drawing minimizes its changes to a point where it can be perceived as visually stable. This thesis presents how the visual stability of a dynamic graph drawing affects the user experience and the efficiency of the visual search when tracking actors or network attributes over time. For this purpose, a framework to support flexible visualization techniques was developed. It served as the platform to evaluate existing layout ad- justment techniques. Such an evaluation combined the use of questionnaires to gather information about the user experience; an eye-tracking device to record the eye move- ments and a new mathematical model to appropriately quantify the visual stability of dynamic graph drawings. The results obtained suggest that there is a trade-off between the user experience and the efficiency of the visual search, which depends on the visual stability of a dynamic graph drawing. On the one hand, dynamic graph drawings with higher levels of visual stability provide a satisfying user experience in tracking tasks. Nonetheless, they are inefficient in terms of the visual search. On the other hand, dynamic graph drawings with lower levels of visual stability, do not provide a satisfying user experience in tracking tasks but considerably improve the efficiency of the visual search. These findings were used to develop visually stable metaphors, aiming at exploring network attributes over time. Such metaphors rely on features like scaling or highlighting to improve the efficiency of the visual search