Presentation on theme: "Visualizing Social Networks for Health and Public Safety Zachary Jacobson, Health Canada Olivier Dagenais & Ben Houston October-2005."— Presentation transcript:
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 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 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
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
Zacks 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 Healthtrack an ongoing outbreak manage it CounterInteltrack [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.
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….