From the August 2014 issue

Dynamic Network Visualization with Extended Massive Sequence Views

By Stef van den Elzen, Danny Holten, Jorik Blaas, and Jarke J. van Wijk

Featured articleNetworks are present in many fields such as finance, sociology, and transportation. Often these networks are dynamic: they have a structural as well as a temporal aspect. In addition to relations occurring over time, node information is frequently present such as hierarchical structure or time-series data. We present a technique that extends the Massive Sequence View ( msv) for the analysis of temporal and structural aspects of dynamic networks. Using features in the data as well as Gestalt principles in the visualization such as closure, proximity, and similarity, we developed node reordering strategies for the msv to make these features stand out that optionally take the hierarchical node structure into account. This enables users to find temporal properties such as trends, counter trends, periodicity, temporal shifts, and anomalies in the network as well as structural properties such as communities and stars. We introduce the circular msv that further reduces visual clutter. In addition, the (circular) msv is extended to also convey time-series data associated with the nodes. This enables users to analyze complex correlations between edge occurrence and node attribute changes. We show the effectiveness of the reordering methods on both synthetic and a rich real-world dynamic network data set.

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IEEE Transactions on Visualization and Computer Graphics (TVCG) is a monthly journal that publishes research on subjects related to computer graphics and visualization techniques, systems, software, hardware, and user interface issues. Read the full scope of TVCG