Visual Computing

University of Konstanz
2014 18th International Conference on Information Visualisation

Graph Exploration by Multiple Linked Metric Views

A. Panagiotidis, M. Burch, O. Deussen, D. Weiskopf, T. Ertl
Teaser of Graph Exploration by Multiple Linked Metric Views

Material

Paper (.pdf, 3.5 MB)

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.

BibTeX

@inproceedings{Panagiotidis2014GraphExplorationMultiple,
  author     = {A. Panagiotidis and M. Burch and O. Deussen and D. Weiskopf and T. Ertl},
  booktitle  = {2014 18th International Conference on Information Visualisation},
  doi        = {10.1109/IV.2014.51},
  issn       = {1550-6037},
  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},
  month      = {jul},
  pages      = {19-26},
  title      = {Graph Exploration by Multiple Linked Metric Views},
  year       = {2014},
  url        = {http://graphics.uni-konstanz.de/publikationen/Panagiotidis2014GraphExplorationMultiple},
}