Dynamic Graph Visualisation

Visualising dynamic graphs in particular for larger data sets and when additional information is available such as a hierarchical organisation of the objects is a challenging task. Many data dimensions have to be represented at the same time:

• the graph vertices (objects)
• the edges induced by the graph (relations)
• the weights of the edges
• the inclusion edges induced by the hierarchy
• the evolution of the graph over time

We surveyed the field of dynamic graph visualisation in a EuroVis 2014 state-of-the-art report (STAR). More than 120 papers have already been published in this growing field of research, among them about 60 unique visualisation techniques. We classified the techniques into a simple hierarchical taxonomy and made our literature collection also available as an interactive database.

• The state of the art in visualizing dynamic graphs [pdf]
• Interactive literature database

Taxonomy of dynamic graph visualisation techniques.

Traditional approaches use a time-to-time mapping and show time-varying graph data as animated sequences of node-link diagrams. Though this visualisation strategy is very intuitive, it also has some drawbacks:

• if the graphs are very dense, i.e. have many edges, visual clutter occurs caused by many edge crossings
• animation leads to cognitive efforts for a viewer to preserve his mental map
• sophisticated layout algorithms are needed to circumvent the two former mentioned problems that have a high runtime complexity

In our research, we avoid a time-to-time mapping and encode the time dimension into space instead. We use stacked graphical colour-coded elements to show weighted time-varying relations and we show links only implicitly by different orientations instead of direct explicit links as in node-link diagrams.

Layered TimeRadarTree visualisation showing more than 6,000,000 data points of an evolving directed and weighted graph

TimeRadarTree visualisation for soccer match results of 14 years in a part of Europe

The thumbnail view for the goalkeeper showing all weighted relations to all other players in a specific time interval

Our approach allows easily exploring a time-varying graph data set for trends, countertrends and anomalies and has many benefits:

• visual clutter is reduced by showing the links implicitly
• cognitive efforts are reduced and the mental map is preserved by using static images
• interactive features can easily be applied
• run time complexities are reduced and graphs can be added on-the-fly.