The document provides an overview of social network analysis principles and tools for visualizing networks, discussing how influence is difficult to measure, the importance of data cleaning, and demonstrating example networks including right and left wing blogs, a Facebook social network, and internet undersea cables. It also discusses interactive network visualization tools like D3, Gephi, and NodeXL and stresses that visualizations should not look too "sciency."

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

9. New Scientist Twitter Page
Made with NodeXL
Shows Twitter icons
Indicates tweet diffusion and polarization
Source: ConnectedAction.net

11. Facebook Social Networks
Made with Sigma.js / Gephi toolkit
Interactive browser-based
Most nets show social roles as clusters
Source: Hogan and Melville

16. Facebook Global Network
Made with R
Beautiful and signifying
Note the absence of Russia, China and Africa
Source: Facebook.com

18. Internet Undersea Cables
Package unknown
Relevance of geography
Artistic rendering shows much more
Sources: Caida, Telegeography

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