Visual analysis of temporal networks comprises an effective way to understand the network dynamics, facilitating 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, thus considering the underlying network structure and addressing 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.

中文翻译：

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一种用于网络可视化的在线非均匀时间分片方法

时间网络的可视化分析是了解网络动态的有效方法，有助于识别模式、异常和其他网络属性，从而快速做出决策。然而，现实世界网络中的数据量可能会由于边缘重叠而导致布局具有高度的视觉混乱。这在所谓的流网络中尤其重要，其中边缘不断地到达（在线）和非平稳分布。可以操纵所有三个网络维度，即节点、边和时间，以减少这种混乱并提高可读性。本文提出了一种在线非均匀时间切片方法，从而考虑了底层网络结构并解决了流网络分析问题。我们使用两个真实世界的网络进行了实验，以将我们的方法与均匀和非均匀时间切片策略进行比较。结果表明，我们的方法自动选择时间片，有效减少事件爆发期间的视觉混乱。因此，基于全局时间模式识别的决策变得更快、更可靠。