DNC: Dynamic Neighborhood Change Faithfulness Metrics
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Date
2022
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Faithfulness metrics measure how faithfully a visualization displays the ground truth information of the data. For example, neighborhood faithfulness metrics measure how faithfully the geometric neighbors of vertices in a graph drawing represent the ground truth neighbors of vertices in the graph. This paper presents a new dynamic neighborhood change (DNC) faithfulness metric for dynamic graphs to measure how proportional the geometric neighborhood change in dynamic graph drawings is to the ground truth neighborhood change in dynamic graphs. We validate the DNC metrics using deformation experiments, demonstrating that it can accurately measure neighborhood change faithfulness in dynamic graph drawings. We then present extensive comparison experiments to evaluate popular graph drawing algorithms using DNC, to recommend which layout obtains the highest neighborhood change faithfulness on a variety of dynamic graphs.
Description
@inproceedings{10.2312:evs.20221092,
booktitle = {EuroVis 2022 - Short Papers},
editor = {Agus, Marco and Aigner, Wolfgang and Hoellt, Thomas},
title = {{DNC: Dynamic Neighborhood Change Faithfulness Metrics}},
author = {Cai, Shijun and Meidiana, Amyra and Hong, Seok-Hee},
year = {2022},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-184-7},
DOI = {10.2312/evs.20221092}
}