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Networks: A Crash Course at Local Social Summit


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Networks: A Crash Course at Local Social Summit

  1. 1. SNA 101:Not just reading tea leaves Dr Bernie Hogan [@blurky] Research Fellow, Oxford Internet Institute University of Oxford LocalSocialSummit, November 13, 2012
  2. 2. What do you already know? Diffusion Happens Nets look niceSome are influencers Science has potential
  3. 3. Some Principles• Influence is not an obvious process.• Central people are not always the most popular people (but usually are)• Visualizations can be useful, but this takes work. Sciency is bull$#!†.
  4. 4. Some Limits• Estimating selection versus influence is extremely tough, even for the best.• Visualizing more than a couple thousand points? Congrats, its now art!• A link is not always a meaningful link• Data cleaning is the worst part. Seriously.
  5. 5. Tools!• Interactive is hot! D3 and Sigma.js (or just javascript/html5) are the future of interactive network visualization• Gephi [Cross-Platform] creates very spiffy diagrams and has great layouts for dustballs: ForceAtlas, ForceAtlas2,YuFan Hu, FR, Nooverlap• NodeXL [Windows] has great data management features and a couple neat visualization features. See: for inspiration.
  6. 6. Some networksAnd why you should care
  7. 7. Right and Left Wing BlogsMade with GUESSNice sharp PDF.Two blobs show clear partition.Source: Adamic and Glance 2005
  8. 8. New Scientist Twitter PageMade with NodeXLShows Twitter iconsIndicates tweet diffusion and polarizationSource:
  9. 9. Facebook Social NetworksMade with Sigma.js / Gephi toolkitInteractive browser-basedMost nets show social roles as clustersSource: Hogan and Melville
  10. 10. Facebook Global NetworkMade with RBeautiful and signifyingNote the absence of Russia, China and AfricaSource:
  11. 11. Internet Undersea CablesPackage unknownRelevance of geographyArtistic rendering shows much moreSources: Caida, Telegeography
  12. 12. Obesity over timeMade with SONIAOverly clutteredBad visual variables
  13. 13. Networks shouldn’t look Sciency Source: Christakis & Fowler. N Engl J Med 2007;357:370-9.
  14. 14. Two Network Demos• Network Visualization App • •• NodeXL •
  15. 15. Network 1 Goals• Overview• What do clusters mean?• Who is considered more central?
  16. 16. Network 2 Goals• Is there cohesion among the group? • Many blobs or few? Core-periphery or multi-core.• Are certain people broadcasters?• How can we accentuate the story?• List people by number of tweets?
  17. 17. Thank You Bernie Hogan Research Fellow, OII Twitter: @blurky