Daniel W. Archambault

Animation, Small Multiples, and the Effect of Mental Map Preservation in Dynamic Graphs

In this paper, we present the results of a human-computer interaction experiment that compared the performance of the animation of dynamic graphs to the presentation of small multiples and the effect that mental map preservation had on the two conditions. Questions used in the experiment were selected to test both local and global properties of graph evolution over time. The data sets used in this experiment were derived from standard benchmark data sets of the information visualization community. We found that small multiples gave significantly faster performance than animation overall and for each of our five graph comprehension tasks. In addition, small multiples had significantly more errors than animation for the tasks of determining sets of nodes or edges added to the graph during the same timeslice, although a positive time-error correlation coefficient suggests that, in this case, faster responses did not lead to more errors. This result suggests that, for these two tasks, animation is preferable if accuracy is more important than speed. Preserving the mental map under either the animation or the small multiples condition had little influence in terms of error rate and response time.

GrouseFlocks: Steerable Exploration of Graph Hierarchy Space

Several previous systems allow users to interactively explore a large input graph through cuts of a superimposed hierarchy. This hierarchy is often created using clustering algorithms or topological features present in the graph. However, many graphs have domain-specific attributes associated with the nodes and edges, which could be used to create many possible hierarchies providing unique views of the input graph. GrouseFlocks is a system for the exploration of this graph hierarchy space. By allowing users to see several different possible hierarchies on the same graph, the system helps users investigate graph hierarchy space instead of a single fixed hierarchy. GrouseFlocks provides a simple set of operations so that users can create and modify their graph hierarchies based on selections. These selections can be made manually or based on patterns in the attribute data provided with the graph. It provides feedback to the user within seconds, allowing interactive exploration of this space.

TopoLayout: Multilevel Graph Layout by Topological Features

We describe TopoLayout, a feature-based, multilevel algorithm that draws undirected graphs based on the topological features they contain. Topological features are detected recursively inside the graph, and their subgraphs are collapsed into single nodes, forming a graph hierarchy. Each feature is drawn with an algorithm tuned for its topology. As would be expected from a feature-based approach, the runtime and visual quality of TopoLayout depends on the number and types of topological features present in the graph. We show experimental results comparing speed and visual quality for TopoLayout against four other multilevel algorithms on a variety of data sets with a range of connectivities and sizes. TopoLayout frequently improves the results in terms of speed and visual quality on these data sets

Fully automatic visualisation of overlapping sets

Visualisation of taxonomies and sets has recently become an active area of research. Many application fields now require more than a strict classification of elements into a hierarchy tree. Euler diagrams, one of the most natural ways of depicting intersecting sets, may provide a solution to these problems.

Visual Exploration of Complex Time-Varying Graphs

Quasi-trees, namely graphs with tree-like structure, appear in many application domains, including bioinformatics and computer networks. Our new SPF approach exploits the structure of these graphs with a two-level approach to drawing, where the graph is decomposed into a tree of biconnected components. The low-level biconnected components are drawn with a force-directed approach that uses a spanning tree skeleton as a starting point for the layout. The higher-level structure of the graph is a true tree with meta-nodes of variable size that contain each biconnected component. That tree is drawn with a new area-aware variant of a tree drawing algorithm that handles high-degree nodes gracefully, at the cost of allowing edge-node overlaps. SPF performs an order of magnitude faster than the best previous approaches, while producing drawings of commensurate or improved qualityMany graph drawing and visualization algorithms, such as force-directed layout and line-dot rendering, work very well on relatively small and sparse graphs. However, they often produce extremely tangled results and exhibit impractical running times for highly non-planar graphs with large edge density. And very few graph layout algorithms support dynamic time-varying graphs; applying them independently to each frame produces distracting temporally incoherent visualizations. We have developed a new visualization technique based on a novel approach to hierarchically structuring dense graphs via stratification. Using this structure, we formulate a hierarchical force-directed layout algorithm that is both efficient and produces quality graph layouts. The stratification of the graph also allows us to present views of the data that abstract away many small details of its structure. Rather than displaying all edges and nodes at once, resulting in a convoluted rendering, we present an interactive tool that filters edges and nodes using the graph hierarchy and allows users to drill down into the graph for details. Our layout algorithm also accommodates time-varying graphs in a natural way, producing a temporally coherent animation that can be used to analyze and extract trends from dynamic graph data. For example, we demonstrate the use of our method to explore financial correlation data for the U.S. stock market in the period from 1990 to 2005. The user can easily analyze the time-varying correlation graph of the market, uncovering information such as market sector trends, representative stocks for portfolio construction, and the interrelationship of stocks over time.

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

We 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 cubes 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.

Mental map preservation helps user orientation in dynamic graphs

We present the results of a formal experiment that tests the ability of a participant to orient themselves in a dynamically evolving graph. Examples of these tasks include finding a specific location or route between two locations. We find that preserving the mental map for the tasks tested is significantly faster and produces fewer errors. As the number of targets increase, this result holds.

Difference map readability for dynamic graphs

Difference maps are one way to show changes between timeslices in a dynamic graph. They highlight, using colour, the nodes and edges that were added, removed, or persisted between every pair of adjacent timeslices. Although some work has used difference maps for visualization, no user study has been performed to gauge their performance. In this paper, we present a user study to evaluate the effectiveness of difference maps in comparison with presenting the evolution of the dynamic graph over time on three interfaces. We found evidence that difference maps produced significantly fewer errors when determining the number of edges inserted or removed from a graph as it evolves over time. Also, difference maps were significantly preferred on all tasks.

The mental map and memorability in dynamic graphs

In dynamic graph drawing, preserving the mental map, or ensuring that the location of nodes do not change significantly as the information evolves over time is considered an important property by algorithm designers. Many prior experiments have attempted to verify this principle, with surprisingly little success. These experiments have used several different algorithmic methods, a variety of graph interpretation questions on both real and fabricated data, and different presentation methods. However, none of the results have conclusively demonstrated the importance of mental map preservation on task performance. Our experiment measures the efficacy of the dynamic graph drawing in a different manner: we look at how memorable the evolving graph is, rather than how easy it is to interpret. As observed in the previous studies, we found no significant difference in terms of response time or error rate when preserving the mental map. While preserving the mental map is a good idea in principle, we find that it may not always support performance. However, our qualitative data suggests that, in terms of the users perception, preserving the mental map makes memorability tasks easier. Our qualitative data also suggests that there may be two features of the dynamic graph drawing that may assist in their memorability: interesting subgraphs that remain visible over time and interesting patterns in node movement. The former is supported by preserving the mental map while the latter is not.

ThemeCrowds: multiresolution summaries of twitter usage

Users of social media sites, such as Twitter, rapidly generate large volumes of text content on a daily basis. Visual summaries are needed to understand what groups of people are saying collectively in this unstructured text data. Users will typically discuss a wide variety of topics, where the number of authors talking about a specific topic can quickly grow or diminish over time, and what the collective is saying about the subject can shift as a situation develops. In this paper, we present a technique that summarises what collections of Twitter users are saying about certain topics over time. As the correct resolution for inspecting the data is unknown in advance, the users are clustered hierarchically over a fixed time interval based on the similarity of their posts. The visualisation technique takes this data structure as its input. Given a topic, it finds the correct resolution of users at each time interval and provides tags to summarise what the collective is discussing. The technique is tested on a large microblogging corpus, consisting of millions of tweets and over a million users.