1. Visualizing Social Networks for Health and Public SafetyZachary Jacobson, Health CanadaOlivier Dagenais & Ben Houston October-2005

2. [N/X]n welcome, well come • Social network analysis/analyses for public safety • Health [infection, esp.] • Security [counter-terror intel, esp.] • Some firsts, this time • Moving from visualizing information/knowledge to networks • More people came here to listen than to speak !!

3. Invited provocations • A clear advantage • 20 minute guillotine • Try to leave time for questions. • Provocations will be [e-]published • Get your e-copy to Margaret! • And thanks to the provocaturs!

4. Knowledge [information] Discovery • Institutional collaborators, fellow travellers • Health Canada • DND • NATO RTP • CNSC • IAEA

5. outline • Introduction • [this is/was it] • Social network properties • Scale-free concept • Applications • VITA • 9-11 simulator • [later] breakout instructions • To work!

6. Social networks • Understand relations among individuals a.k.a. links and nodes analysis • Nodes, or individuals: e.g., • People perhaps in a situation • A hurricane • A battle • A corporation • Computers in a network • Asocial networks • Ideas in an argument • Neurons in a cortex

7. Random Scale-free Majority of nodes have one or two links, but a few nodes have a large number of links. Most nodes have approximately the same number of links. More than 60% of nodes (green) can be reached from the five most connected nodes (red) in the scale-free network compared with only 27% in the random network. Both networks contain 130 nodes and 430 links. Source: Barabási, Physicsweb, July 2001

8. In a scale-free network these highly connected nodes are known as “hubs” In the WWW, hubs might be websites such as Yahoo or Google Among hollywood actors the hubs are actors that have worked with the most people Among scientific collaboration networks, the hubs are the scientists who have collaborated with the most people or co-authored papers with the most people In cells the hubs are the most connected molecules such as water or ATP, ADP In an infectious disease transmission network, hubs are the people who are in contact with a large number of susceptible people In a random network, a virus, or idea, gets established more readily but can be eradicated. In a scale free network most outbreaks fail, but some may never eradicated.

9. SNA gossip • Social networkers divided • Old guard, social scientists • New wave, physicists and other hard scientists A new-fangled idea

10. Zack’s personal prediction and take-home message: • social nets often fractal and scale-free in nature, in Nature. • from the www to SARS spread to needle exchange to neurones in the brain • an important unifying principle • Here to stay

11. Social network analytic tools • Advanced tools exist • Vienna is an established centre • Pajek tool and development group [algorithmic] • Also in US • UICNet [rigorous] • Both have visual presentation available, static nets • INSNA sunbelt conferences • Need for dynamic analysts • Health—track an ongoing outbreak • manage it • CounterIntel—track [e-]communications in real time • See the developing hotspots • Develop usable assistants • Implement

12. VITA - a visual front end for document search systems • to discover effective methods of identifying relationships among documents and assisting in reducing document search complexity • Now available for research/analysis • Search control by the user • Search results presentation under user control • initially engine-independent • Now Google-based • Accept other engines with minimal work • Various prototypes.

13. VITA Concept—aid for knowledge discovery

14. question 3 fixed planes Concepts [search terms] Hits [web pages] VITA General Layout Mechanism

15. VITA-gExample

16. VITA-DExample • A.Q. Khan queries

17. Computer-Assisted Contact Tracing Logical next step uses in health and counterterror [also network management & protection]

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20. Generic Network Visualization: Applications for NATO This working group was focused at developing a taxonomy and framework of generic network properties which are required for the display on a Common Operational Picture and decision support.

21. Objectives • Development of a network visualisation framework to be used by NATO • Development of a common language to describe networks and to enable interoperability

22. NATO Needs on Network Analysis/Visualization • Counterterrorism • Knowledge Management • Information Assurance • Logistic Support Management • Disease Management • Infrastructure Security • Correlation of interconnected networks • etc.

23. What do we need to see about the network[s]? • General properties • Topology • Node identification [usually • Link identification [rarely] • Network variables • Varying within the network • Intersection[s] with other, disparate networks • E.g., load links to telephone lines

24. Visualisation Issues • Human Factors • Colors • Temporal information • Automation • Cluttering • Symbology • etc.

25. 9|11 cell Epidemic simulator Another speaker Live --

26. Generic network visualization:Conclusion: • task oriented • same generic framework can be used for most types of networks • Network Analysis can be focused on nodes, links, etc. • Easily moved into any of several applications

27. In order to have something available in the heat of the moment… .