Transcript Workshop Introduction – Social Network Analysis for Public Safety

Visualizing Social Networks for
Health and Public Safety
Zachary Jacobson, Health Canada
Olivier 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

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A clear advantage
20 minute guillotine
 Try to leave time for questions.
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Provocations will be [e-]published
 Get your e-copy to Margaret!
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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]
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Social network properties
Scale-free concept
Applications
 VITA
 9-11 simulator

[later] breakout instructions
 To work!
Social networks

Understand relations among individuals
a.k.a. links and nodes analysis
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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
Most nodes have approximately the same
number of links.
Scale-free
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
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Pajek tool and development group [algorithmic]
 Also in US
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UICNet [rigorous]
 Both have visual presentation available, static nets
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INSNA sunbelt conferences
Need for dynamic analysts
 Health—track an ongoing outbreak
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manage it
 CounterIntel—track [e-]communications in real time
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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
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Search control by the user
Search results presentation under user control
initially engine-independent
 Now Google-based
 Accept other engines with minimal work
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Various prototypes.
VITA Concept—aid for knowledge discovery
VITA General Layout Mechanism
question
3 fixed
planes
Concepts [search terms]
Hits [web pages]
VITA-g Example
VITA-D Example

A.Q. Khan queries
Computer-Assisted Contact
Tracing
Logical next step
uses in health and counterterror
[also network management & protection]
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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
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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
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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]
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Network variables
 Varying within the network
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Intersection[s] with other, disparate networks
 E.g., load links to telephone lines
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Visualisation Issues
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Human Factors
Colors
Temporal information
Automation
Cluttering
Symbology
etc.
Live -
9|11 cell
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Epidemic simulator
 Another speaker
Generic network visualization:
Conclusion:
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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… .