Danny Holten

Force-directed edge bundling for graph visualization

Graphs depicted as node‐link diagrams are widely used to show relationships between entities. However, node‐link diagrams comprised of a large number of nodes and edges often suffer from visual clutter. The use of edge bundling remedies this and reveals high‐level edge patterns. Previous methods require the graph to contain a hierarchy for this, or they construct a control mesh to guide the edge bundling process, which often results in bundles that show considerable variation in curvature along the overall bundle direction. We present a new edge bundling method that uses a self‐organizing approach to bundling in which edges are modeled as flexible springs that can attract each other. In contrast to previous methods, no hierarchy is used and no control mesh. The resulting bundled graphs show significant clutter reduction and clearly visible high‐level edge patterns. Curvature variation is furthermore minimized, resulting in smooth bundles that are easy to follow. Finally, we present a rendering technique that can be used to emphasize the bundling.

Understanding Execution Traces Using Massive Sequence and Circular Bundle Views

The use of dynamic information to aid in software understanding is a common practice nowadays. One of the many approaches concerns the comprehension of execution traces. A major issue in this context is scalability: due to the vast amounts of information, it is a very difficult task to successfully find your way through such traces without getting lost. In this paper, we propose the use of a novel trace visualization method based on a massive sequence and circular bundle view, constructed with scalability in mind. By means of three usage scenarios that were conducted on three different software systems, we show how our approach, implemented in a tool called EXTRAVIS, is applicable to the areas of trace exploration, feature location, and feature comprehension.

A user study on visualizing directed edges in graphs

Graphs are often visualized using node-link representations: vertices are depicted as dots, edges are depicted as (poly)lines connecting two vertices. A directed edge running from vertex A to B is generally visualized using an arrow representation: a (poly)line with a triangular arrowhead at vertex B. Although this representation is intuitive, it is not guaranteed that a user is able to determine edge direction as quickly and unambiguously as possible; alternative representations that exhibit less occlusion and visual clutter might be better suited. To investigate this, we developed five additional directed-edge representations using combinations of shape and color. We performed a user study in which subjects performed different tasks on a collection of graphs using these representations and combinations thereof to investigate which representation is best in terms of speed and accuracy. We present our initial hypotheses, the outcome of the user studies, and recommendations regarding directed-edge visualization.

Visual comparison of hierarchically organized data

We provide a novel visualization method for the comparison of hierarchically organized data. Our technique visualizes a pair of hierarchies that are to be compared and simultaneously depicts how these hierarchies are related by explicitly visualizing the relations between matching subhierarchies. Elements that are unique to each hierarchy are shown, as well as the way in which hierarchy elements are relocated, split or joined. The relations between hierarchy elements are visualized using Hierarchical Edge Bundles (HEBs). HEBs reduce visual clutter, they visually emphasize the aforementioned splits, joins, and relocations of subhierarchies, and they provide an intuitive way in which users can interact with the relations. The focus throughout this paper is on the comparison of different versions of hierarchically organized software systems, but the technique is applicable to other kinds of hierarchical data as well. Various data sets of actual software systems are used to show how our technique can be employed to easily spot splits, joins, and relocations of elements, how sorting both hierarchies with respect to each other facilitates comparison tasks, and how user interaction is supported.

Execution trace analysis through massive sequence and circular bundle views

Preprint of article published in: Journal of Systems and Software (Elsevier), 81 (12), 2008; doi:10.1016/j.jss.2008.02.068 An important part of many software maintenance tasks is to gain a sufficient level of understanding of the system at hand. The use of dynamic information to aid in this software understanding process is a common practice nowadays. A major issue in this context is scalability: due to the vast amounts of information, it is a very difficult task to successfully navigate through the dynamic data contained in execution traces without getting lost. In this paper, we propose the use of two novel trace visualization techniques based on the massive sequence and circular bundle view, which both reflect a strong emphasis on scalability. These techniques have been implemented in a tool called Extravis. By means of distinct usage scenarios that were conducted on three different software systems, we show how our approach is applicable in three typical program comprehension tasks: trace exploration, feature location, and top-down analysis with domain knowledge.

Evaluation of cluster identification performance for different PCP variants

Parallel coordinate plots (PCPs) are a well‐known visualization technique for viewing multivariate data. In the past, various visual modifications to PCPs have been proposed to facilitate tasks such as correlation and cluster identification, to reduce visual clutter, and to increase their information throughput. Most modifications pertain to the use of color and opacity, smooth curves, or the use of animation. Although many of these seem valid improvements, only few user studies have been performed to investigate this, especially with respect to cluster identification. We performed a user study to evaluate cluster identification performance – with respect to response time and correctness – of nine PCP variations, including standard PCPs. To generate the variations, we focused on covering existing techniques as well as possible while keeping testing feasible. This was done by adapting and merging techniques, which led to the following novel variations. The first is an effective way of embedding scatter plots into PCPs. The second is a technique for highlighting fuzzy clusters based on neighborhood density. The third is a spline‐based drawing technique to reduce ambiguity. The last is a pair of animation schemes for PCP rotation. We present an overview of the tested PCP variations and the results of our study. The most important result is that a fair number of the seemingly valid improvements, with the exception of scatter plots embedded into PCPs, do not result in significant performance gains.

An extended evaluation of the readability of tapered, animated, and textured directed-edge representations in node-link graphs

We present the results of a study comparing five directed-edge representations for use in 2D, screen-based node-link diagrams. The goal of this work is to extend the understanding of tradeoffs and best practices for the representation of edges in directed graphs and to help practitioners choose among different options. Our work applies to graphs in which directed links are depicted using lines connecting the nodes. We tested five different edge representations chosen carefully based on user feedback to thoroughly cover the directed-edge design space. We also investigated how the use of pattern compression affects performance and subjective user preference. The article presents detailed results regarding the significant performance and preference differences between directed-edge representations and provides practical recommendations on their use.

Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration

We propose a visual analytics approach for the exploration and analysis of dynamic networks. We consider snapshots of the network as points in high-dimensional space and project these to two dimensions for visualization and interaction using two juxtaposed views: one for showing a snapshot and one for showing the evolution of the network. With this approach users are enabled to detect stable states, recurring states, outlier topologies, and gain knowledge about the transitions between states and the network evolution in general. The components of our approach are discretization, vectorization and normalization, dimensionality reduction, and visualization and interaction, which are discussed in detail. The effectiveness of the approach is shown by applying it to artificial and real-world dynamic networks.

Dynamic Network Visualization withExtended Massive Sequence Views

Networks are present in many fields such as finance, sociology, and transportation. Often these networks are dynamic: they have a structural as well as a temporal aspect. In addition to relations occurring over time, node information is frequently present such as hierarchical structure or time-series data. We present a technique that extends the Massive Sequence View ( msv) for the analysis of temporal and structural aspects of dynamic networks. Using features in the data as well as Gestalt principles in the visualization such as closure, proximity, and similarity, we developed node reordering strategies for the msv to make these features stand out that optionally take the hierarchical node structure into account. This enables users to find temporal properties such as trends, counter trends, periodicity, temporal shifts, and anomalies in the network as well as structural properties such as communities and stars. We introduce the circular msv that further reduces visual clutter. In addition, the (circular) msv is extended to also convey time-series data associated with the nodes. This enables users to analyze complex correlations between edge occurrence and node attribute changes. We show the effectiveness of the reordering methods on both synthetic and a rich real-world dynamic network data set.

Visual Realism for the Visualization of Software Metrics

The visualization techniques used in current software visualization frameworks make use of a limited set of graphical elements to highlight relevant aspects of a software system. Typical examples of such elements are text, simple geometric shapes and uniform color fills. Although human visual perception enables rapid processing of additional visual cues like shading and texture, they are not used. We contend that such 2D and 3D computer graphics techniques for achieving visual realism can be used to increase the information throughput of software visualization techniques. Visualization results are presented to show how treemaps, cushions, color, texture, and bump mapping can be used to visualize software metrics of hierarchically organized elements of a software system