Creative approaches to dynamic network visualization

In a previous blog post, we discussed some of the best approaches to dynamic network visualization that we found during our research on how to handle time-based data.

Visualizing Bitcoin transactions reveals interesting activity patterns
The time bar: a powerful way to filter and summarize time-based data in our KeyLines and ReGraph graph visualization toolkits

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

They’re largely techniques developed for specific ‘niche’ datasets, so they don’t translate well to other use cases. But they’re all interesting in their own right. Let’s take a closer look.

1. Tree Rings

tree rings

Farrugia, 2011 [PDF]

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 time-based 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.

It’s definitely unique, but probably not sufficiently intuitive for most users.

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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 gets 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. Gantt charts and Sankey diagrams

If you’re handling time-based data, these specialist visualizations help to portray how connections and dependencies evolve.

Gantt charts and Sankey diagrams give aggregated, and often static, views of events and connections over time.

Gantt charts & Sankey diagrams

The drawback with such an aggregated view is that you only see a high-level picture and don’t get to drill down into the detail. Gantt charts and Sankey diagrams are fixed images. They lack the dynamic quality users get from an interactive visualization experience.

5. 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 audience’s attention is being split between four / five different charts. That’s a lot of information coming from a lot of different angles.

Visualizing dynamic data the smart way

If you’ve tried some or all of these creative options, you’re probably ready for a tool that provides a fully-interactive way to analyze your time-based data.

Our time bar functionality makes it easy to focus on specific time periods in your connected data, compare activity or spot trends and outliers.

Add selection lines to area plots to show how specific values evolve over time
Time bar selection lines on area plots summarize the important values in your data

If you need to take time-based investigation one step further, our KronoGraph SDK offers fully-scalable timeline visualizations that reveal how events unfold.

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This post was originally published some time ago. It’s still popular, so we’ve updated it with fresh content to keep it useful and relevant.

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