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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 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 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 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] f035 f002 f030 m013 m106 m211 f023 f201 f010 f034 m026 f033 m201 f006 f015 m017 f202 m018 fx36 fx07 m012 m107 f009 f017 f021 f514 f104 m202 fx13 m212 mx04 f103 m200 f019 m526 m203 f900 f008 f024 m301 f011 m204 fx21 m206 m025 m112 m551 f022 mx11 mx12 m209 m304 m207 m208 f029 fx03 f012 m016 m537 f014 fx21 m306 f533 mx10 f536 f020 f541 m523 f007 mx05 mx01 m546 m210 f013 m014 fx12 m102 m101 f038 m019 mx06 f010 f002 f030 m013 Sexual network member f202 f201 f033 f034 f035 f015 Member attending bar fx07 m026 m106 m012 m107 f009 fx13 Bar m201 fx36 m202 f006 f103 f019 f104 f514 mx14 mx04 f008 m017 m112 m203 f017 m526 f011 m002 m206 fx06 f900 f106 f023 m010 m025 f014 m018 m551 f021 m204 m212 m211 f025 m209 m207 m302 f024 m301 f022 m208 f016 m007 f012 m214 fx21 f004 m200 m110 m111 mx06 f007 f541 m016 f003 f533 mx01 m023 m523 f013 f020 f546 f038 m019 f536 m101 mx05 m210 fx12 m102 f205 m104 m014 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… .