On Time and Space: An Experimental Study on Graph Structural and Temporal Encodings

TL;DR

This study evaluates how different structural and temporal encodings in dynamic graph visualization affect user performance and preferences, revealing that node-link with auto animation offers the best overall efficiency and accuracy.

Contribution

It provides an empirical comparison of structural and temporal encodings in dynamic graph visualization, highlighting optimal combinations for user tasks.

Findings

Node-link with auto animation is fastest and most accurate.

Matrices support lower-level entity tasks better.

Animation with playback control is most preferred.

Abstract

Dynamic networks reflect temporal changes occurring to the graph's structure and are used to model a wide variety of problems in many application fields. We investigate the design space of dynamic graph visualization along two major dimensions: the network structural and temporal representation. Significant research has been conducted evaluating the benefits and drawbacks of different structural representations for static graphs, however, few extend this comparison to a dynamic network setting. We conduct a study where we assess the participants' response times, accuracy, and preferences for different combinations of the graph's structural and temporal representations on typical dynamic network exploration tasks, with and without support of common interaction methods. Our results suggest that matrices provide better support for tasks on lower-level entities and basic interactions require longer response times while increasing accuracy. Node-link with auto animation proved to be the quickest and most accurate combination overall, while animation with playback control the most preferred temporal encoding.