Increasing the utility of quantitative empirical studies for meta-analysis
Proceedings of the 2008 Workshop on BEyond time and errors: novel evaLuation methods for Information Visualization
Human-centered visualization environments
Human-centered visualization environments
Comparing benchmark task and insight evaluation methods on timeseries graph visualizations
Proceedings of the 3rd BELIV'10 Workshop: BEyond time and errors: novel evaLuation methods for Information Visualization
A comparison of benchmark task and insight evaluation methods for information visualization
Information Visualization - Special issue on Evaluation for Information Visualization
Visualizing genome expression and regulatory network dynamics in genomic and metabolic context
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
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The most common approach to support analysis of graphs with associated time series data include: overlay of data on graph vertices for one timepoint at a time by manipulating a visual property (e.g. color) of the vertex, along with sliders or some such mechanism to animate the graph for other timepoints. Alternatively, data from all the timepoints can be overlaid simultaneously by embedding small charts into graph vertices. These graph visualizations may also be linked to other visualizations (e.g., parallel co-ordinates) using brushing and linking. This paper describes a study performed to evaluate and rank graph+timeseries visualization options based on users' performance time and accuracy of responses on predefined tasks. The results suggest that overlaying data on graph vertices one timepoint at a time may lead to more accurate performance for tasks involving analysis of a graph at a single timepoint, and comparisons between graph vertices for two distinct timepoints. Overlaying data simultaneously for all the timepoints on graph vertices may lead to more accurate and faster performance for tasks involving searching for outlier vertices displaying different behavior than the rest of the graph vertices for all timepoints. Single views have advantage over multiple views on tasks that require topological information. Also, the number of attributes displayed on nodes has a non trivial influence on accuracy of responses, whereas the number of visualizations affect the performance time.