Visual analysis of temporal networks comprises an effective way to understand the network dynamics. It facilitates the identification of patterns, anomalies, and other network properties, thus resulting in fast decision making. The amount of data in real-world networks, however, may result in a layout with high visual clutter due to edge overlapping. This is particularly relevant in the so-called streaming networks, in which edges are continuously arriving (online) and in non-stationary distribution. All three network dimensions, namely node, edge, and time, can be manipulated to reduce such clutter and improve readability. This paper presents an online and nonuniform timeslicing method that enhances temporal and streaming network analyses. We conducted experiments using two real-world networks to compare our method against uniform and nonuniform timeslicing strategies. The results show that our method automatically selects timeslices that effectively reduce visual clutter in periods with bursts of events. As a consequence, decision making based on the identification of global temporal patterns becomes faster and more reliable.

时态网络的可视化分析是了解网络动态的有效方法。它有助于识别模式，异常和其他网络属性，从而快速做出决策。但是，由于边缘重叠，实际网络中的数据量可能会导致布局具有很高的视觉混乱度。这在所谓的流网络中尤其重要，在流网络中，边缘不断到达（在线）并且处于非平稳分布。所有三个网络维度，即节点，边缘和时间可以进行操作，以减少此类混乱并提高可读性。本文提出了一种在线的，非均匀的时间分片方法，该方法可以增强时间和流网络分析。我们使用两个真实世界的网络进行了实验，以比较我们的方法与统一和非统一时间划分策略。结果表明，我们的方法会自动选择可在事件突发期间有效减少视觉混乱的时间片。结果，基于全局时间模式识别的决策变得更快，更可靠。