text: Several techniques for visualization of dynamic graphs are based on different spatial arrangements of a temporal sequence of node-link diagrams. Many studies in the literature have investigated the importance of maintaining the user's mental map across this temporal sequence, but usually each layout is considered as a static graph drawing and the effect of user interaction is disregarded. We conducted a task-based controlled experiment to assess the effectiveness of two basic interaction techniques: the adjustment of the layout stability and the highlighting of adjacent nodes and edges. We found that generally both interaction techniques increase accuracy, sometimes at the cost of longer completion times, and that the highlighting outclasses the stability adjustment for many tasks except the most complex ones.
  @cite_14: Identifying, tracking and understanding changes in dynamic networks are complex and cognitively demanding tasks. We present GraphDiaries, a visual interface designed to improve support for these tasks in any node-link based graph visualization system. GraphDiaries relies on animated transitions that highlight changes in the network between time steps, thus helping users identify and understand those changes. To better understand the tasks related to the exploration of dynamic networks, we first introduce a task taxonomy, that informs the design of GraphDiaries, presented afterwards. We then report on a user study, based on representative tasks identified through the taxonomy, and that compares GraphDiaries to existing techniques for temporal navigation in dynamic networks, showing that it outperforms them in terms of both task time and errors for several of these tasks.
  @cite_33: Visualization has proven to be a useful tool for understanding network structures. Yet the dynamic nature of social media networks requires powerful visualization techniques that go beyond static network diagrams. To provide strong temporal network visualization tools, designers need to understand what tasks the users have to accomplish. This paper describes a taxonomy of temporal network visualization tasks. We identify the 1) entities, 2) properties, and 3) temporal features, which were extracted by surveying 53 existing temporal network visualization systems. By building and examining the task taxonomy, we report which tasks are well covered by existing systems and make suggestions for designing future visualization tools. The feedback from 12 network analysts helped refine the taxonomy.
  @cite_7: Networks that evolve over time, or dynamic graphs, have been of interest to the areas of information visualization and graph drawing for many years. Typically, the structure of the dynamic graph evolves as vertices and edges are added or removed from the graph. In a multivariate scenario, however, attributes play an important role and can also evolve over time. In this chapter, we characterize and survey methods for visualizing temporal multivariate networks. We also explore future applications and directions for this emerging area in the fields of information visualization and graph drawing.
  @cite_11: In this chapter, we describe tasks that are typically encountered during visual multivariate network analysis. First, we present an overview of the entities and properties of multivariate networks and discuss a taxonomy for general visualization tasks. We next describe a framework for multivariate network tasks and show how these tasks can be composed of lower-level tasks of the general taxonomy. We also include several real-world examples of multivariate network tasks as illustrations.
summary: A profound understanding of analytical tasks is a necessary prerequisite to design novel visualization techniques as well as evaluate existing ones. @cite_33 propose a task taxonomy for dynamic graphs along three different axes: graph entities, temporal features, and properties (structural and domain-specific). According to @cite_14 , each task can be understood as a question containing references to two dimensions and requiring an answer in the third one. In this way, they distinguish between topological tasks, temporal tasks, and behavioural tasks. Archambault and Purchase @cite_7 structure their taxonomy along two dimension, mostly aiming at assessing the importance of the mental map. They distinguish between local and global tasks, and between distinguishable and undistinguishable tasks (depending on whether graph entities need to be distinguished from each other or can be aggregated). A task taxonomy for multivariate networks can be found in @cite_11 .