%0 Conference Paper %1 6902875 %A Panagiotidis, Alexandros %A Burch, Michael %A Deussen, Oliver %A Weiskopf, Daniel %A Ertl, Thomas %B International Conference on Information Visualisation (IV) %D 2014 %K drawing graph histogramm link linked metrics myown node view %P 19-26 %R 10.1109/IV.2014.51 %T Graph Exploration by Multiple Linked Metric Views %X 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.

@inproceedings{6902875, 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.}, added-at = {2015-06-01T16:16:38.000+0200}, author = {Panagiotidis, Alexandros and Burch, Michael and Deussen, Oliver and Weiskopf, Daniel and Ertl, Thomas}, biburl = {https://www.bibsonomy.org/bibtex/2b15d5de1c4094e4c09548927a27204e9/alexandrospanagiotidis}, booktitle = {International Conference on Information Visualisation (IV)}, doi = {10.1109/IV.2014.51}, interhash = {c07dbcea9c887d448b50725d8e8d438e}, intrahash = {b15d5de1c4094e4c09548927a27204e9}, issn = {1550-6037}, keywords = {drawing graph histogramm link linked metrics myown node view}, month = {July}, pages = {19-26}, timestamp = {2015-06-01T16:16:38.000+0200}, title = {Graph Exploration by Multiple Linked Metric Views}, year = 2014 }