This document discusses graph visualization techniques. It begins by explaining that data must be visually encoded to be understood. Different types of data, such as quantitative, ordinal, and nominal data, are best visualized using different encodings like position, length, area, hue, and saturation. Common graph layouts include nodes and edges, matrices, edge bundling, and hive plots. The document outlines common mistakes to avoid, such as using 3D, poor color choice, lack of labels or legends, and not including tooltips or interactivity. It promotes starting with the question, meaningful encoding, interaction, filters, aggregation, and using high-quality tools to untangle complex graphs.

1. Graph Visualisation
Unpicking the Hairball
Joe Parry

2. What does data look like?

7. Data is invisible

8. To be seen, data must be
encoded

9. How?

11. Visual encoding by data type
Quantitative
Quantitative
Ordinal
Ordinal
Nominal
Nominal
Position
Position
Density
Hue
Angle
Saturation
Density
Slope
Hue
Saturation
Area
Length
Shape
Density
Angle
Length
Saturation
Slope
Angle
Hue
Less Accurate
Position
Length
More Accurate
Area
Slope
Shape
Shape
Area

12. Graph Encoding

13. Edge
Vertex
Vertex

14. Edge
Vertex
Vertex

15. The Hairball!

16. Matrix

17. Edge bundling

18. Hive plots
Hive Plots

19. ..but the most intuitive is still..

20. Edge
Vertex
Vertex

21. Common Graph Visualization Mistakes
• 3D
– Occlusion problems
– Difficult to navigate
– Hard to print
• Bad Colour Choice
– Red/Green
– Rainbow
• No Labels
– What is this vertex anyway?!
• No Legend
– What are these categories?

22. Common Graph Visualization Mistakes
• No Tooltip
– Need more information on hover
• No emphasis of important nodes
• Black backgrounds
– (Only appropriate for users in the dark)
• Bad navigation
– Should use all of mouse, touch,
gestures
• No Interaction!
– Users need to interact with /
manipulate the data

23. Demos!

24. Weapons against the hairball

25. 1. Start with the Question

26. 2. Meaningful Visual Encoding

27. 3. Interaction

28. 4. Visual Filters

29. 5. Aggregation

30. 6. Use a great tool ;-)

31. All demos made with
To see the demos that were shown during this
presentation, get in touch:
keylines.com/contact