An Online and Nonuniform Timeslicing Method for Network Visualisation
This work addresses visual clutter issues for analysts of streaming networks, but it is incremental as it builds on existing timeslicing strategies.
The paper tackled the problem of visual clutter in streaming network visualizations by introducing an online and nonuniform timeslicing method, which automatically selects timeslices to reduce clutter during bursts of events, resulting in faster and more reliable decision-making based on global temporal patterns.
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.