Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> DESIGN GUIDELINES <s> We present CiteWiz, an extensible framework for visualization of scientific citation networks. The system is based on a taxonomy of citation database usage for researchers, and provides a timeline visualization for overviews and an influence visualization for detailed views. The timeline displays the general chronology and importance of authors and articles in a citation database, whereas the influence visualization is implemented using the Growing Polygons technique, suitably modified to the context of browsing citation data. Using the latter technique, hierarchies of articles with potentially very long citation chains can be graphically represented. The visualization is augmented with mechanisms for parent-child visualization and suitable interaction techniques for interacting with the view hierarchy and the individual articles in the dataset. We also provide an interactive concept map for keywords and co-authorship using a basic force-directed graph layout scheme. A formal user study indicates that CiteWiz is significantly more efficient than traditional database interfaces for high-level analysis tasks relating to influence and overviews, and equally efficient for low-level tasks such as finding a paper and correlating bibliographical data. <s> BIB001 </s> Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines <s> DESIGN GUIDELINES <s> 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. <s> BIB002
Having formalized a model of hierarchical aggregation for visualization and having surveyed existing examples for supporting aggregation in visualization techniques, we now derive a set of general guidelines for designing and implementing such techniques: BIB001 . Dependencies for the aggregate are the sum of their children's dependencies and the shadows give a visual affordance that the aggregates can be expanded. Fig. 14 . Pyramid aggregation hierarchy for the ZAME BIB002 system. Each aggregate node represents four nodes in the level below. G1. Entity budget. Maintain an entity budget; G2. Visual summary. Aggregates should convey information about underlying data; G3. Visual simplicity. Aggregates should be clean and simple; G4. Discriminability. Aggregates should be distinguishable from data items; G5. Fidelity. Beware that abstractions may lie; and G6. Interpretability. Aggregate items only so much so that the aggregation is still correctly interpretable within the visual mapping. We describe each of these guidelines in greater depth below.