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

Visual Similarity Perception of Directed Acyclic Graphs: A Study on Influencing Factors

While visual comparison of directed acyclic graphs (DAGs) is commonly encountered in various disciplines (e.g., finance, biology), knowledge about humans' perception of graph similarity is currently quite limited. By graph similarity perception we mean how humans perceive commonalities and differences in graphs and herewith come to a similarity judgment. As a step toward filling this gap the study reported in this paper strives to identify factors which influence the similarity perception of DAGs. In particular, we conducted a card-sorting study employing a qualitative and quantitative analysis approach to identify 1) groups of DAGs that are perceived as similar by the participants and 2) the reasons behind their choice of groups. Our results suggest that similarity is mainly influenced by the number of levels, the number of nodes on a level, and the overall shape of the graph.Comment: Graph Drawing 2017 - arXiv Version; Keywords: Graphs, Perception, Similarity, Comparison, Visualizatio

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

Using Eye Movement Data Visualization to Enhance Training of Air Traffic Controllers: A Dynamic Network Approach

The Federal Aviation Administration (FAA) forecasted substantial increase in the US air traffic volume creating a high demand in Air Traffic Control Specialists (ATCSs). Training times and passing rates for ATCSs might be improved if expert ATCSs’ eye movement (EM) characteristics can be utilized to support effective training. However, effective EM visualization is difficult for a dynamic task (e.g. aircraft conflict detection and mitigation) that includes interrogating multi-element targets that are dynamically moving, appearing, disappearing, and overlapping within a display. To address the issues, a dynamic network-based approach is introduced that integrates adapted visualizations (i.e. time-frame networks and normalized dot/bar plots) with measures used in network science (i.e. indegree, closeness, and betweenness) to provide in-depth EM analysis. The proposed approach was applied in an aircraft conflict task using a high-fidelity simulator; employing the use of veteran ATCSs and pseudo pilots. Results show that, ATCSs’ visual attention to multi-element dynamic targets can be effectively interpreted and supported through multiple evidences obtained from the various visualization and associated measures. In addition, we discovered that fewer eye fixation numbers or shorter eye fixation durations on a target may not necessarily indicate the target is less important when analyzing the flow of visual attention within a network. The results show promise in cohesively analyzing and visualizing various eye movement characteristics to better support training. 

GraphDiaries: Animated Transitions and Temporal Navigation for Dynamic Networks

International audienceIdentifying, tracking and understanding changes in dynamic networks are complex and cognitively demanding tasks. We present GraphDiaries, a visual interface designed to improve support for these tasks in any node-link based graph visualization system. GraphDiaries relies on animated transitions that highlight changes in the network between time steps, thus helping users identify and understand those changes. To better understand the tasks related to the exploration of dynamic networks, we first introduce a task taxonomy, that informs the design of GraphDiaries, presented afterwards. We then report on a user study, based on representative tasks identified through the taxonomy, and that compares GraphDiaries to existing techniques for temporal navigation in dynamic networks, showing that it outperforms them in terms of both task time and errors for several of these tasks