We present the zoomable adjacency matrix explorer (ZAME), a visualization tool for exploring graphs at a scale of millions of nodes and edges. ZAME is based on an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and panning with interactive performance from an overview to the most detailed views. Several components work together in the ZAME tool to make this possible. Efficient matrix ordering algorithms group related elements. Individual data cases are aggregated into higher-order meta-representations. Aggregates are arranged into a pyramid hierarchy that allows for on-demand paging to GPU shader programs to support smooth multiscale browsing. Using ZAME, we are able to explore the entire French Wikipedia - over 500,000 articles and 6,000,000 links - with interactive performance on standard consumer-level computer hardware.

Abstract: This article presents the Zoomable Adjacency Matrix Explorer (ZAME), a visualization tool for exploring networks at a scale of millions of nodes and tens of millions of edges. ZAME presents an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and panning with interactive performance from the most summary,to the most detailed views. Several components work together to make this performance possible and the results meaningful. A “pyramid” of aggregated views paged on demand,to OpenGL GPU shader programs supports smooth multiscale browsing in huge datasets. Efficient matrix ordering algorithms group related elements to make the views meaningful. Using ZAME, we can ex- plore the entire French Wikipedia, over 500,000 articles and 6,000,000 links, with interactive performance on standard consumer-level computer hardware. Key-words: Large-scale graph visualization, matrix-based representation, node-link dia-

We present a visual exploration of the field of human–computer interaction (HCI) through the author and article metadata of four of its major conferences: the ACM conferences on Computer-Human Interaction (CHI), User Interface Software and Technology, and Advanced Visual Interfaces and the IEEE Symposium on Information Visualization. This article describes many global and local patterns we discovered in this data set, together with the exploration process that produced them. Some expected patterns emerged, such as that–like most social networks–coauthorship and citation networks exhibit a power-law degree distribution, with a few widely collaborating authors and highly cited articles. Also, the prestigious and long-established CHI conference has the highest impact (citations by the others). Unexpected insights included that the years when a given conference was most selective are not correlated with those that produced its most highly referenced articles and that influential authors have distinct patterns of collaboration. An interesting sidelight is that methods from the HCI field–exploratory data analysis by information visualization and direct-manipulation interaction–proved useful for this analysis. They allowed us to take an open-ended, exploratory approach, guided by the data itself. As we answered our original questions, new ones arose; as we confirmed patterns we expected, we discovered refinements, exceptions, and fascinating new ones.

We describe interactions between kinetic (moving) and static information displays. We have implemented "moxel" kinetic displays in a classic discovery platform with many standard information visualization and analytic tools, and experimented with interactions between them. Moxels, which generalize pixels, are an advanced, moving, form of iconographic display of the kind first developed in static form by Pickett and White (1966). As with the static graphic icons of those early displays, moxels provide a way of mapping together in one image multiple data variables, but with potentially more potency with in-place motion. We show examples of how the two kinds of displays have been integrated, and discuss issues with the integration of dynamic and static visualizations in a single environment. We discuss several interaction paradigms between them including linked brushing, multiple selections, and operations on selected regions.

To be most useful, evaluation metrics should be based on detailed observation and effective analysis of a full spectrum of system use. Because observation is costly, ideally we want a system to provide in-depth data collection with allied analyses of the key user interface elements. We have developed a visualization and analysis platform [1] that automatically records user actions and states at a high semantic level [2 and 3], and can be directly restored to any state. Audio and text annotations are collected and indexed to states, allowing users to comment on their current situation as they work, and/or as they review the session. These capabilities can be applied to usability evaluation of the system, describing problems they encountered, or to suggest improvements to the environment. Additionally, computed metrics are provided at each state [3, 4, and 5]. We believe that the metrics and the associated history data will allow us to deduce patterns of data exploration, to compare users, to evaluate tools, and to understand in a more automated approach the usability of the visualization system as a whole.

To be most useful, evaluation requires detailed observation and effective analysis of a full spectrum of system use. We have developed an approach and architecture for in-depth data collection and analysis of all use of a visualization system. User interface components in a large visualization and analysis platform automatically record user actions, and can restore previous system states on demand. Audio and text annotations are collected and indexed to states, allowing users to find a comment and restore the system state in which they made it; then explore actions before and after. History is visible as data; so a variety of visual displays and analysis techniques may be used to develop insights about the user's experience. States of any part of the interface may be analyzed separately. Actions are categorized in a taxonomy as the user interface is built, allowing comparison of similar patterns in all tools. History data can co-exist with other data during data exploration, supporting further individual or group data exploration

Most visualization taxonomies are designed to help designers and users choose appropriate analysis and visualization techniques for the data being displayed; even automate the generation of visualizations. We have developed a taxonomy with a different purpose: to support the analysis and modeling of the human data exploration process. User actions in a large information visualization and analysis platform are recorded by custom user interface (UI) models that save a history of operations. Each model value is labeled with a taxonomic category. When the global history is viewed as a dataset, these categories provide a standard framework for comparing similar operations throughout the system. Like several other taxonomies, ours is based upon the information visualization pipeline model. However it differs in its focus on user actions, ignoring specifics of data structure. This approach maximizes discovery of common interaction patterns across tools (visualization and analysis) and systems, such as highlighting subsets of the data correlated with certain interactions and allows one to tailor the taxonomy for the specific discovery platform. A key contribution of this work is automating both the recording of actions and their labeling in a principled taxonomy, which greatly reduces the effort required to collect long semanticallylabeled histories. Because the approach of taxonomically-labeled UI models that record actions should be usable for any interactive system, it could also facilitate comparing interaction patterns across domains. CR Categories: I.3.6 [Computer Graphics]: Methodology and Techniques – Interaction techniques, Standards H.5.2 [Information Systems]: User Interfaces – Theory and methods, Standardization, User interface management systems (UIMS), Evaluation/methodology, Graphical user interfaces

Although there are a number of visualization systems to choose from when analyzing data, only a few of these allow for the integration of other visualization and analysis techniques. There are even fewer visualization toolkits and frameworks from which one can develop ones own visualization applications. Even within the research community, scientists either use what they can from the available tools or start from scratch to define a program in which they are able to develop new or modified visualization techniques and analysis algorithms. Presented here is a new general-purpose platform for constructing numerous visualization and analysis applications. The focus of this system is the design and experimentation of new techniques, and where the sharing of and integration with other tools becomes second nature. Moreover, this platform supports multiple large data sets, and the recording and visualizing of user sessions. Here we introduce the Universal Visualization Platform (UVP) as a modern data visualization and analysis system.

This article presents the Zoomable Adjacency Matrix Explorer (ZAME), a visualization tool for exploring networks at a scale of millions of nodes and tens of millions of edges. ZAME presents an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and panning with interactive performance from the most summary to the most detailed views. Several components work together to make this performance possible and the results meaningful. A ``pyramid'' of aggregated views paged on demand to OpenGL GPU shader programs supports smooth multiscale browsing in huge datasets. Efficient matrix ordering algorithms group related elements to make the views meaningful. Using ZAME, we can explore the entire French Wikipedia, over 500,000 articles and 6,000,000 links, with interactive performance on standard consumer-level computer hardware.