Methods for visualizing dynamic networks

24th February, 2015

A couple of weeks ago we discussed some of the best approaches to dynamic network visualization that we found during our research for the time bar.

This week, we’ve selected some of the more creative approaches to come from the world of academia.

Largely they are techniques developed for specific ‘niche’ datasets, and therefore do not translate to other use cases. But all are interesting in their own right… So:

1. Tree Rings

tree rings

Farrugia, 2011

This approach moves away from representing networks in a force-directed layout, instead showing nodes and links displayed across a series of time rings. In this way, the temporal element of the data is brought into the foreground, and users understand the data’s evolution by shape, rather than by animation, highlighting or another method.

Our view: it’s definitely unique, but probably not sufficiently intuitive for most users.

2. 3D layers

3d layers

Brandes and Corman, 2002

This approach overlays incremental network charts so the user can observe differences with each layer. It removes some of the challenges of other time slice approaches, but get’s very complex quite quickly.

3. Consecutive Matrices


Henry and Fekete

You’re probably aware of how a matrix can be used to represent a network: nodes along the X&Y axis, with the corresponding cell filled if a relationship is present. This approach generates multiple matrices for comparison. We’re not convinced it’s much simpler than other approaches, and adds further complication by removing the intuitive node-link model.

4. The data viz mashup


Hadlack, 2011

If you can’t find a single perfect approach that works for you, combine several! The only problem with this is the audiences attention is being split between four / five different charts. That’s a lot of information coming from a lot of different angles.

Find out more

If you need help visualizing your dynamic networks, take a look at our time bar functionality. Get in touch for more information – we’d love to hear from you.

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