This document discusses graph visualization and effective strategies for visually encoding and representing graph data. It explains that data must be encoded visually through quantitative, ordinal, or nominal mappings to properties like position, length, angle, area, density, hue, and saturation. The document then overviews common graph visualization techniques like matrices, edge bundling, hive plots, and node-link diagrams. It concludes by outlining design principles for effective graph visualization, such as starting with the question, using meaningful visual encodings, enabling interaction, employing visual filters, utilizing aggregation, and using high-quality visualization tools.

1. Joe Parry
Graph Visualisation
Unpicking the Knot

2. What does data look like?

7. Data is invisible

8. To be seen, data must be
encoded

9. How?

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

12. Graph Encoding

13. Matrix

14. Edge bundling

15. Hive plots
Hive Plots

16. Classic node-link diagram

17. Demos!

18. Design principles for effective
graph visualisation

19. 1. Start with the Question

20. 2. Meaningful Visual Encoding

21. 3. Interaction

22. 4. Visual Filters

23. 5. Aggregation

24. 6. Use a great tool ;-)

25. All demos made with
joe@keylines.com