visualizing social networks for health and public safety zachary jacobson, health canada olivier...
TRANSCRIPT
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]
f010
f015
m017
f034fx36
f033
f009
m201
fx07
m202
m012
m013
f201
m551
f900
f514
m526
m206
f019
f202
f017
m207
f020
m209
f533f014
f022
m203
m204f011
m016
f038m019
m210
m208
m025
f012
f035
m026
fx13
f030
f008
f103
f104
m107
f006
m106
f002
m112
mx04
m211
f021
m212
m018
f023
fx21
m301
f024
m200
m546
mx06
f541
m014
f013
m523
mx01
m102
mx05
fx12
f536
m101
f007
fx21
mx11
fx03
mx12
mx10m306
f029
m304
m537
f010
m026
f015
m017
f034
fx36
f033
f009
m201
fx07
m202
m012
f035
m013
f201
m551
f900
f514
m526m206
f019
f202
f017
m207
f012
f020
m209
f533
f014
f022
m203
m204
f011
m016
f038m019
m210
m208
m025
f002 f030
m106
f006
m107
f104f103
fx13
f008m112
m007
mx06 f541
mx04
m102
mx05
f013
fx12
f004
m014
mx01
f536
m523
m101
f546
m212
fx06
fx21
m301f024
m200
mx14
m302f025
f021m018
f023
m211
m002
m010f106
f007m111m110
m023
f205
m214
f016
f003
m104
Sexual network member
Member attending
bar
Bar
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… .