  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.
