Dynamic networks -- networks that change over time -- can be categorized into two types: offline dynamic networks, where all states of the network are known, and online dynamic networks, where only the past states of the network are known. Research on staging animated transitions in dynamic networks has focused more on offline data, where rendering strategies can take into account past and future states of the network. Rendering online dynamic networks is a more challenging problem since it requires a balance between timeliness for monitoring tasks -- so that the animations do not lag too far behind the events -- and clarity for comprehension tasks -- to minimize simultaneous changes that may be difficult to follow. To illustrate the challenges placed by these requirements, we explore three strategies to stage animations for online dynamic networks: time-based, event-based, and a new hybrid approach that we introduce by combining the advantages of the first two. We illustrate the advantages and disadvantages of each strategy in representing low- and high-throughput data and conduct a user study involving monitoring and comprehension of dynamic networks. We also conduct a follow-up, a think-aloud study combining monitoring and comprehension with experts in dynamic network visualization. Our findings show that animation staging strategies that emphasize comprehension do better for participant response times and accuracy. However, the notion of ``comprehension'' is not always clear when it comes to complex changes in highly dynamic networks, requiring some iteration in staging that the hybrid approach affords. Based on our results, we make recommendations for balancing event-based and time-based parameters for our hybrid approach.

中文翻译：

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在线动态网络的分阶段动画策略

动态网络——随时间变化的网络——可以分为两种类型：离线动态网络，其中网络的所有状态都是已知的；在线动态网络，其中只有网络的过去状态是已知的。在动态网络中进行动画过渡的研究更多地关注离线数据，其中渲染策略可以考虑网络的过去和未来状态。渲染在线动态网络是一个更具挑战性的问题，因为它需要在监控任务的及时性之间取得平衡——这样动画不会落后于事件太远——以及理解任务的清晰度——以尽量减少可能困难的同时变化跟随。为了说明这些要求带来的挑战，我们探索了三种为在线动态网络制作动画的策略：基于时间的、基于事件的，以及我们通过结合前两者的优点引入的一种新的混合方法。我们说明了每种策略在表示低吞吐量和高吞吐量数据方面的优缺点，并进行了一项涉及动态网络监控和理解的用户研究。我们还与动态网络可视化专家进行了一项后续研究，这是一项将监控和理解相结合的思考式研究。我们的研究结果表明，强调理解的动画分期策略对参与者的响应时间和准确性更好。然而，当涉及到高度动态网络中的复杂变化时，“理解”的概念并不总是很清楚，需要混合方法提供的一些迭代。