Visualizing Social Networks for Health and Public SafetyZachary Jacobson, Health CanadaOlivier Dagenais & Ben Houston

October-2005

[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 !!

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!

Knowledge [information] Discovery

Institutional collaborators, fellow travellers

Health Canada DND NATO RTP CNSC IAEA

outline

Introduction [this is/was it]

Social network properties Scale-free concept Applications

VITA 9-11 simulator

[later] breakout instructions To work!

Social networks

Understand relations among individualsa.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

Random Scale-free

Most nodes have approximately the same number of links.

Majority of nodes have one or two links, but a few nodes have a large 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

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.

SNA gossip

Social networkers divided

Old guard, social scientists New wave, physicists and other hard

scientists

A new-fangled idea

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

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

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.

VITA Concept—aid for knowledge discovery

3 fixedplanes3 fixedplanes

questionquestion

Hits [web pages]Hits [web pages]

Concepts [search terms]Concepts [search terms]

VITA General Layout Mechanism

VITA- Example

VITA- Example

A.Q. Khan queries

Computer-Assisted Contact Tracing

Logical next stepuses in health and counterterror

[also network management & protection]

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Sexual network member

Member attending

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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.

Objectives

Development of a network visualisation framework to be used by NATO

Development of a common language to describe networks and to enable interoperability

NATO Needs on Network Analysis/Visualization

Counterterrorism Knowledge Management Information Assurance Logistic Support Management Disease Management Infrastructure Security Correlation of interconnected networks etc.

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

Visualisation Issues

Human Factors Colors Temporal information Automation Cluttering Symbology etc.

Live --

9|11 cell Epidemic simulator Another speaker

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

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