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Networks: A Crash Course at Local Social Summit

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SNA 101: Not just reading tea leaves Dr Bernie Hogan [@blurky] Research Fellow, Oxford Internet Institute University of Oxford LocalSocialSummit, November 13, 2012
What do you already know? Diffusion Happens Nets look nice Some are influencers Science has potential
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$#!†.
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.
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: nodexlgraphgallery.com for inspiration.
Some networks And why you should care
Right and Left Wing Blogs Made with GUESS Nice sharp PDF. Two blobs show clear partition. Source: Adamic and Glance 2005
New Scientist Twitter Page Made with NodeXL Shows Twitter icons Indicates tweet diffusion and polarization Source: ConnectedAction.net
Facebook Social Networks Made with Sigma.js / Gephi toolkit Interactive browser-based Most nets show social roles as clusters Source: Hogan and Melville
Facebook Global Network Made with R Beautiful and signifying Note the absence of Russia, China and Africa Source: Facebook.com
Internet Undersea Cables Package unknown Relevance of geography Artistic rendering shows much more Sources: Caida, Telegeography
Obesity over time Made with SONIA Overly cluttered Bad visual variables
Networks shouldn’t look Sciency Source: Christakis & Fowler. N Engl J Med 2007;357:370-9.
Two Network Demos • Network Visualization App • http://blogs.oii.ox.ac.uk/vis • http://apps.facebook.com/namegencollege • NodeXL • http://nodexl.codeplex.com/
Network 1 Goals • Overview • What do clusters mean? • Who is considered more central?
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?
Thank You Bernie Hogan Research Fellow, OII http://people.oii.ox.ac.uk/hogan Twitter: @blurky bernie.hogan@oii.ox.ac.uk

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Networks: A Crash Course at Local Social Summit

  • 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. What do you already know? Diffusion Happens Nets look nice Some are influencers Science has potential
  • 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. 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. 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: nodexlgraphgallery.com for inspiration.
  • 6. Some networks And why you should care
  • 7. Right and Left Wing Blogs Made with GUESS Nice sharp PDF. Two blobs show clear partition. Source: Adamic and Glance 2005
  • 8.
  • 9. New Scientist Twitter Page Made with NodeXL Shows Twitter icons Indicates tweet diffusion and polarization Source: ConnectedAction.net
  • 10.
  • 11. Facebook Social Networks Made with Sigma.js / Gephi toolkit Interactive browser-based Most nets show social roles as clusters Source: Hogan and Melville
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  • 15.
  • 16. Facebook Global Network Made with R Beautiful and signifying Note the absence of Russia, China and Africa Source: Facebook.com
  • 17.
  • 18. Internet Undersea Cables Package unknown Relevance of geography Artistic rendering shows much more Sources: Caida, Telegeography
  • 19.
  • 20.
  • 21. Obesity over time Made with SONIA Overly cluttered Bad visual variables
  • 22. Networks shouldn’t look Sciency Source: Christakis & Fowler. N Engl J Med 2007;357:370-9.
  • 23. Two Network Demos • Network Visualization App • http://blogs.oii.ox.ac.uk/vis • http://apps.facebook.com/namegencollege • NodeXL • http://nodexl.codeplex.com/
  • 24. Network 1 Goals • Overview • What do clusters mean? • Who is considered more central?
  • 25. 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?
  • 26. Thank You Bernie Hogan Research Fellow, OII http://people.oii.ox.ac.uk/hogan Twitter: @blurky bernie.hogan@oii.ox.ac.uk