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Seeing Graphs - How to successfully visualize connected data
The document discusses graph visualization and provides guidance on visualizing connected data through graphs. It covers topics like graph modeling, why graphs are useful, design options for graph visualization including node-link diagrams, matrix views and hive plots. It also discusses strategies for larger networks, interaction models, using animation, and provides a reference architecture for graph visualization systems.
Seeing Graphs - How to successfully visualize connected data
- 1. Joe Parry Seeing Graphs Howto successfully visualize connected data
- 2. Agenda • The graphdesign process • Interaction models • Why animate? • Strategies for visualizing larger networks • A reference architecture
- 3. GRAPH MODELLING + DataVisualisation = Graph Visualisation
- 4. Why use thegraph data model? Data models are generally interchangeable, but each have their own strengths in certain. The graph data model is good for…
- 5. • Social interactions •Dependency management • Organizational structures • Connectivity of systems • Biological processes • Financial transactions • Communications • http://keylines.com/why
- 6. Good in whatsense? Graph models answer questions: • what depends on what • how do things flow • where are the risks • impact analysis
- 7. A quick clarification… Inany data, there is not ‘one graph’. Any single data set can be modelled in many different ways: Perhaps more suitable for understanding social networks? Probably a more accurate picture of how email sending works
- 8. Graph Modelling + DATAVISUALISATION = Graph Visualisation
- 9. Data is invisible Tobe seen, data must be VISUALLY ENCODED
- 10. There are manydifferent options for encoding data We need to understand which works best for our audience Fortunately, people have already researched the most accurate encoding methods…
- 11. Quantitative Ordinal Nominal MoreAccurate 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. Data visualisation isa DESIGN PROCESS
- 13. Graph Modelling + DataVisualisation = GRAPH VISUALISATION
- 14. There are severaldesign options for graph visualisation
- 15. Matrix • All nodeshave a column & a row • A dot is put in the middle if a link exists • Good for finding clusters • But requires a lot of cross referencing to understand the details
- 16. Edge bundling • Nodesare around the edge • Links between the nodes are bundled to show patterns • Detail can become spread out • Computationally difficult to create
- 17. Hive plots Hive Plots •Nodes are grouped into categories • Links between nodes are added • This can help display huge networks with a lessened ‘hairball’ effect • But generally Hive Plots are less intuitive
- 18. Classic node-link diagram •Intuitive • Easy to understand • The most ‘successful’ option for conveying data
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- 20. Why is interactionneeded? • The user needs to drive their own focus • Allows the user to see details on demand • Allows the user to add or remove bits of the graph
- 21. Interactions users expect •Zooming • Multiple node selection • Hover and tooltip events • Context menus • Gestures & tablet support • Expand on demand • Searching • Commands
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- 23. Why animate? • Usersexpect it • It provides smoother transitions: – eases context switching for users – Allows them to keep a mental model • Animations – can show data – can show dynamics
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- 25. Beyond a certainsize, no automatic layout will help you decipher a graph Your options include:
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- 28. 3. Allow usersto expand
- 29. 4. Link &Node aggregation
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- 31. Graph Database Neo4j Web Server ReferenceArchitecture Document Store Search Index User Authentication (eg Active Directory) Chart Store http://keylines.com/architecture
- 32. Cambridge Intelligence Start-up, foundedMay 2011 We make network visualisation tools (big data projects, fraud, security services)
- 33. All demos madewith [email protected] Questions? Contact Information