Graph Visualization – Unpicking the Hairball - Joe Parry @ GraphConnect London 2013

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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.

Technology

Graph Visualisation
Unpicking the Hairball
Joe Parry

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

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?

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

All demos made with

To see the demos that were shown during this
presentation, get in touch:

keylines.com/contact

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Graph Visualization – Unpicking the Hairball - Joe Parry @ GraphConnect London 2013

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?

10.

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

Graph Visualization – Unpicking the Hairball - Joe Parry @ GraphConnect London 2013

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