Evaluating Multivariate Network Visualization Techniques Using a Validated Design and ...
Evaluating Multivariate Network Visualization Techniques Using a Validated Design and Crowdsourcing Approach
Carolina Nobre, Dylan Wootton, Lane Harrison, Alexander Lex
CHI '20: ACM CHI Conference on Human Factors in Computing Systems
Session: Visualizing trees, networks & paths
Abstract
Visualizing multivariate networks is challenging because of the trade-offs necessary for effectively encoding network topology and encoding the attributes associated with nodes and edges. A large number of multivariate network visualization techniques exist, yet there is little empirical guidance on their respective strengths and weaknesses. In this paper, we describe a crowdsourced experiment, comparing node-link diagrams with on-node encoding and adjacency matrices with juxtaposed tables. We find that node-link diagrams are best suited for tasks that require close integration between the network topology and a few attributes. Adjacency matrices perform well for tasks related to clusters and when many attributes need to be considered. We also reflect on our method of using validated designs for empirically evaluating complex, interactive visualizations in a crowdsourced setting. We highlight the importance of training, compensation, and provenance tracking.
DOI:: https://doi.org/10.1145/3313831.3376381
WEB:: https://chi2020.acm.org/
Remote Presentations for the ACM CHI Conference on Human Factors in Computing Systems 2020.
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