Title :
Graph Exploration by Multiple Linked Metric Views
Author :
Panagiotidis, A. ; Burch, Michel ; Deussen, O. ; Weiskopf, Daniel ; Ertl, Thomas
Author_Institution :
Visualization Res. Center, Univ. of Stuttgart, Stuttgart, Germany
Abstract :
The visualization of relational data by node-link diagrams quickly leads to a degradation of performance at some exploration tasks when the diagrams show visual clutter and overdraw. To address this challenge of large-data graph visualization, we introduce Graph Metric Views, a technique that enriches the visualization of traditional layout strategies for node-link diagrams by additionally allowing an analyst to interactively explore graph-specific metrics such as number of nodes, number of link crossings, link coverage, or degree of orthogonality. To this end, we support an analyst with additional histogram-like representations at the axes of the display space for graph-specific metrics. In this way, a cluttered and densely packed node-link diagram becomes more explorable even for dense graph regions: The user can use the distribution of metric values as an overview and then select regions of interest for further investigation and filtering.
Keywords :
data visualisation; graph theory; relational databases; degree of orthogonality; dense graph regions; display space; exploration tasks; graph exploration; graph metric views; graph-specific metrics; histogram-like representations; large-data graph visualization; layout strategies visualization; link coverage; link crossings; metric values; multiple linked metric views; node-link diagrams; performance degradation; relational data visualization; visual clutter; Clutter; Data visualization; Histograms; Joining processes; Layout; Measurement; Visualization; graphs; metrics; node-link diagrams;
Conference_Titel :
Information Visualisation (IV), 2014 18th International Conference on
Conference_Location :
Paris