1. Network Visualization & Interaction
Jay Thom

2. Outline Introduction Background Related Works Methodology Conclusion
Why is this important?
Background
History
Related Works
Problems
Solutions
Methodology
What I will contribute
Conclusion

3. Why Is This Important? My motivation: Internet Topology Measurement
Our project
Dealing with large amounts of data
How do we interpret it?
Introduction * Background * Related Works * Methodology * Conclusion

4. Generating Massive Data in Daily Life…
Finance
Healthcare
Sensor Networks
Power Grid
National Security
Consumer Data
50,000 GB per second!
Data is meaningless unless we can interpret it!
Introduction * Background * Related Works * Methodology * Conclusion

5. How Humans Assimilate Data…
Introduction * Background * Related Works * Methodology * Conclusion

6. Much Data Representable as Networks
We need to interpret data to make it useful:
Automated algorithms
Data mining
Machine learning
Human domain expertise needed for optimization:
Data needs to be interpreted quickly
Inconsistencies need to be identified
We need to combine automation with interaction
Introduction * Background * Related Works * Methodology * Conclusion

7. History of Data Visualization
Charles Joseph Minard ( ), Napoleon’s Grand Army in Russia
Introduction * Background * Related Works * Methodology * Conclusion

8. Modern Network Graph Visualizations
The Internet
Introduction * Background * Related Works * Methodology * Conclusion

9. Problems…Readability
Ben Shneiderman and Cody Dunne; ”Interactive Network Exploration to Derive Insights”
Readability Metrics:
Crossing number
number of times an edge crosses another edge
Drawing area (smallest bounding box)
smallest possible bounding box looks best
Symmetry groups
find symmetry in the graph and work around it
Minimal bends in edges
keep edges as straight as possible
Minimal edge lengths
Minimize total length of edges
Angular resolution
higher angle resolution is desirable
Slope number
use minimal number of distinct edge slopes
Introduction * Background * Related Works * Methodology * Conclusion

10. Readability - Motifs
N. Karracher et al: ”Visual Techniques to Support Exploratory Analysis of Graph Data”
Cliques
Connectors
Fans
Introduction * Background * Related Works * Methodology * Conclusion

11. Readability - Motifs
Introduction * Background * Related Works * Methodology * Conclusion

12. Problems…Layout Yifan Hu; “Algorithms for Visualizing Large Networks”
Not all graph can be aesthetically embedded into two or three-dimensional space
small world graphs
Graphs exhibiting power law degree distribution
* Graphs of this nature require an interactive system to comprehend and explore
Spring-Electrical Model
Force-directed
Quad-tree
Stress-Strain Models
Stress model
Strain model (classical MDS)
Multi-scale algorithm
Landmark MDS
Pivot MDS
Introduction * Background * Related Works * Methodology * Conclusion

13. Layout – Spring Electrical Models
Force-Directed:
Highly scalable, can lay out millions of nodes in minutes
Does not cope well with pre-defined edge lengths
Introduction * Background * Related Works * Methodology * Conclusion

14. Layout – Spring Electrical Models
Quad-Tree:
Simulates Force-Directed model
Useful when there are vertices far from the main body of the group
Introduction * Background * Related Works * Methodology * Conclusion

15. Layout – Stress/Strain Models
Stress Model:
Better than Force-Directed for pre-defined edge-lengths
Spring forces attach each node to its neighbors
Ideal spring length is pre-defined
Introduction * Background * Related Works * Methodology * Conclusion

16. Layout – Stress/Strain Models
Pre-dates the Stress model
Fits to the inner product of positions
Centers nodes around an origin
Introduction * Background * Related Works * Methodology * Conclusion

17. Problems…Interaction
Question: What is interaction?
Ji Soo Yi et al;
“Toward a Deeper Understanding of the Role of Interaction in Information Visualization
Interaction Categories:
Select
Explore
Reconfigure
Encode
Abstract/Elaborate
Filter
Connect
Introduction * Background * Related Works * Methodology * Conclusion

18. Interaction - Select “Mark something as interesting”
Allows user to mark a data item to keep track of it
Item is highlighted or labeled, and can be easily located after rearrangement
Preceding action to subsequent operations
Introduction * Background * Related Works * Methodology * Conclusion

19. Interaction - Explore “Show me something else”
Rearrange the view to bring out different details
Move smoothly from one view to another
Pan or zoom a view to enhance detail
Introduction * Background * Related Works * Methodology * Conclusion

20. Interaction - Reconfigure
“Show me a different arrangement”
Change the spatial arrangement of representations
Reveal hidden characteristics of data
Provide different perspectives
Introduction * Background * Related Works * Methodology * Conclusion

21. Interaction - Encode “Show me a different representation”
Change the way data is represented
Uncover new aspects of relationships between data points
Change colors, shapes, sizes, chart/graph types
Introduction * Background * Related Works * Methodology * Conclusion

22. Interaction – Abstract/Elaborate
“Show me more or less detail”
Adjust the level of abstraction
Alter representation from overview to detailed view of individual cases
Show sub-trees of a tree
Show tool-tip view (example)
Introduction * Background * Related Works * Methodology * Conclusion

23. Interaction - Filter “Show me something conditionally”
Enable user to change set of data items being presented based on some condition
Show only data of a certain range
Change color of filtered data so that it is still visible but discernable from non-filtered data
Introduction * Background * Related Works * Methodology * Conclusion

24. Interaction - Connect “Show me related items”
Highlight associations and relationships between items
Show hidden items relevant to a specified item
Hover over an item to reveal hidden relationships
Click on item to show item details not visible
Introduction * Background * Related Works * Methodology * Conclusion

25. U.S. Senators by Voting Similarity 2014
Introduction * Background * Related Works * Methodology * Conclusion

26. U.S. Senators by Voting Similarity 2014
Introduction * Background * Related Works * Methodology * Conclusion

27. Visualization Tools Open-Source Graph Visualization Tools: Guess Gephi
NodeXL
Cytoscape
NetworkX
iGraph
SNAP
Sigmajs
D3.js
Linkurious R
Large Graph Layout (LGL)
Cuttlefish
Graphistry
Graph-tool
libSNA
MeerKat
Netlytic
NetworKit
Pajek
SocNetV
Socioviz
Statnet
SUBDUE
Tulip
Visone
GraphChi
Graphviz
JUNG
Introduction * Background * Related Works * Methodology * Conclusion

28. Categorization User interface Programming language
Ease or difficulty to use
Range of tools available
Range of interaction available
Visual quality
Limitations on number of nodes/edges
Overall performance
Introduction * Background * Related Works * Methodology * Conclusion

29. Conclusion Why is this important? History Problems Solutions
What I will contribute
Introduction * Background * Related Works * Methodology * Conclusion