1. Large Dynamic Networks and
Patterns Visualization in NodeXL
Jacopo Cirrone
Graduate Student at University of Catania
(Faculty of Computer Science Engineering)

2. Networks of different genres in the
Real World
Biological,
Social Transportation
Chemical

3. Why Visualization is important?

4. Improving our understanding of
networks
Networks sources Networks graphs
Network.xml
Network.db
Network.txt

5. Improving our understanding of
networks
Clustering
Vizster [Heer 2006]
Discovering the
structure of the
network Infovis Co-authoring Network [Börner et al. 2004]

6. Visualization of Networks that evolve
over time

7. Visualization of Networks that evolve
over time
Whitfield et al, J of. MBC
2002

8. Overview
• Introduction
• Large temporal networks Visualization in
NodeXL
• Significant Anomalies Visualization in NodeXL
• Demonstration
• Conclusion and plan

9. ObamaCare
Twitter Network

10. New Importer for Dynamic Network

11. Dynamic
Networks
Visualization
Time

12. Overview
• Introduction
• Large temporal networks Visualization
• Significant Anomalies Visualization
• Demonstration
• Conclusion and plan

13. Significant Anomalous Patterns
Visualization
o Important Definition:
o Pattern: Connected region of the graph that spans a certain
time interval with score higher than a given threshold
o For instance:
o Highway Network: low average speed on congested
regions
Traffic Reported
Accidents

14. Others Anomalous Patterns
Examples
o Biology: Most essential pathways in a cell
cyclephase? Activation patterns?
o Smart Grid: Energy consumption patterns for
better planning of generation, storage and
transportation.

15. Load Anomalous Patterns (SigSpot)

16. Reported
Accidents
PATTERNS Pattern
Black = Overlap
those edges or
nodes
belonging to Pattern
two or more
different
patterns in the
given time
interval Grey = No Patterns
Pattern

17. Overview
• Introduction
• Large temporal networks Visualization
• Significant Anomalies Visualization
• Demonstration
• Business logic Explanation
• Conclusion and plan

18. Overview
• Introduction
• Large temporal networks Visualization
• Significant Anomalies Visualization
• Demonstration
• Business logic Explanation
• Conclusion and plan

19. Behind the Visualization
o Let’s suppose we have:
o All the Info about the Dynamic Network and the
Patterns in a text file
PROBLEM:

20. PROBLEM:
Behind the visualization – Solution A
This Solution is not efficient for large networks

21. PROBLEM:
Behind the Visualization – Solution B
Berkeley Database
Network.db
or
Patterns.db

22. Behind the Visualization – Solution B
Berkeley Database
QUERY
Refresh Worksheet
Refresh Graph

23. Network-TREE BERKELEY
Generic NODE CONTENT DATABASE
NR NL
Node or Edge Aggregate
Array Sum [,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,]
Array Max [,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,] AGGREGATE [4,6]
Array Min [,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,] QUERY
Array Avg [,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,] 1
[1,8]
2 5
[1,4] [5,8]
3 6
[1,2] [3,4] [5,6] [7,8]
4
[1,1] [2,2] [3,3] [4,4] [5,5] [6,6] [7,7] [8,8]

24. Overview
• Introduction
• Large temporal networks Visualization
• Significant Anomalies Visualization
• Demonstration
• Business logic Explanation
• Conclusion and plan

25. Conclusion
o This extension can be very useful for future
researchers who are interested on:
o Visualization of time evolving networks
o Visualization of patterns within such networks
o We successfully managed networks with
o Several thousands of nodes
o Several thousands of edges
o Tens of thousands of time slices

26. Plan
o Extend the application to allow the user to
import a network with different formats
o Extend the functionalities of patterns
visualization to make the application more
user-friendly:
o User should detect immediately the edges or
nodes belonging to a certain pattern
o User should detect immediately the time interval
where a certain pattern is defined

27. Thanks!
o Collaborators:
o Prof. Alfredo Ferro at Dept of Computer Science
at Catania University
o Misael Mongiovi, Research Scientist at Dept of
Computer Science UC Santa Barbara
o Prof. Ambuj K. Singh at Dept of Computer Science
at UC Santa Barbara
Questions?