Visualization of graphs with associated timeseries data

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.