1. Charting Collections of
Connections in Social
Media:
Creating Maps and
Measures with NodeXL
A project from the Social Media Research Foundation: http://www.smrfoundation.org

2. About Me
Introductions
Marc A. Smith
Chief Social Scientist
Connected Action Consulting Group
Marc@connectedaction.net
http://www.connectedaction.net
http://www.codeplex.com/nodexl
http://www.twitter.com/marc_smith
http://delicious.com/marc_smith/Paper
http://www.flickr.com/photos/marc_smith
http://www.facebook.com/marc.smith.sociologist
http://www.linkedin.com/in/marcasmith
http://www.slideshare.net/Marc_A_Smith
http://www.smrfoundation.org

3. Social Media
(email, Facebook, Twitter,
YouTube, and more)
is all about
connections
from people to people.
3

4. Patterns are
left
behind
4

5. There are many kinds of ties….
Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Edit, Tag, Comment…
http://www.flickr.com/photos/stevendepolo/3254238329

6. Each contains one or more
social networks
World Wide Web

7. Hubs

8. Bridges

9. http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/

10. http://www.flickr.com/photos/amycgx/3119640267/

13. Social Network Theory
http://en.wikipedia.org/wiki/Social_network
• Central tenet
– Social structure emerges from
– the aggregate of relationships (ties)
– among members of a population
• Phenomena of interest
– Emergence of cliques and clusters
– from patterns of relationships
– Centrality (core), periphery (isolates),
Source: Richards, W.
– betweenness (1986). The NEGOPY
• Methods network analysis
program. Burnaby, BC:
– Surveys, interviews, observations, Department of
Communication, Simon
log file analysis, computational Fraser University. pp.7-
analysis of matrices 16
(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)

14. SNA 101
• Node
A
– “actor” on which relationships act; 1-mode versus 2-mode networks
• Edge
B – Relationship connecting nodes; can be directional
C • Cohesive Sub-Group
– Well-connected group; clique; cluster A B D E
• Key Metrics
– Centrality (group or individual measure)
D • Number of direct connections that individuals have with others in the group (usually look at
incoming connections only)
E • Measure at the individual node or group level
– Cohesion (group measure)
• Ease with which a network can connect
• Aggregate measure of shortest path between each node pair at network level reflects
average distance
– Density (group measure)
• Robustness of the network
• Number of connections that exist in the group out of 100% possible
– Betweenness (individual measure)
F G • # shortest paths between each node pair that a node is on
• Measure at the individual node level
• Node roles
– Peripheral – below average centrality C
H – Central connector – above average centrality D
I – Broker – above average betweenness E

15. http://www.flickr.com/photos/marc_smith/sets/72157622437066929/

17. Welser, Howard T., Eric Gleave, Danyel Fisher,
and Marc Smith. 2007. Visualizing the Signatures
of Social Roles in Online Discussion Groups.
The Journal of Social Structure. 8(2).
Experts and “Answer People” Discussion people, Topic setters
Discussion starters, Topic setters

18. Now Available

19. Analogy: Clusters Are Occluded
Hard to count nodes, clusters

20. Separate Clusters Are More Comprehensible

21. Twitter Network for “Microsoft Research”
*BEFORE*

22. Twitter Network for “Microsoft Research”
*AFTER*

23. Goal: Make SNA easier
• Existing Social Network Tools are challenging
for many novice users
• Tools like Excel are widely used
• Leveraging a spreadsheet as a host for SNA
lowers barriers to network data analysis and
display

24. Who we are
People Disciplines Institutions
University Computer Science University of Maryland
Faculty
Students HCI, CSCW Oxford Internet Institute
Industry Machine Learning Stanford University
Independent Information Visualization Microsoft Research
Researchers UI/UX Illinois Institute of
Technology
Developers Social Science/Sociology Connected Action
Network Analysis Cornell
Collective Action Morningside Analytics

25. Social Media Research Foundation
http://smrfoundation.org

26. What we are trying to do:
Open Tools, Open Data, Open Scholarship
• Build the “Firefox of GraphML” – open tools for
collecting and visualizing social media data
• Connect users to network analysis – make
network charts as easy as making a pie chart
• Connect researchers to social media data sources
• Archive: Be the “Allen Very Large Telescope Array”
for Social Media data – coordinate and aggregate
the results of many user’s data collection and
analysis
• Create open access research papers & findings
• Make “collections of connections” easy for users
to manage

27. What we have done: Open Tools
• NodeXL
• Data providers (“spigots”)
– ThreadMill Message Board
– Exchange Enterprise Email
– Voson Hyperlink
– SharePoint
– Facebook
– Twitter
– YouTube
– Flickr

28. What we have done: Open Data
• NodeXLGraphGallery.org
– User generated collection
of network graphs,
datasets and annotations
– Collective repository for
the research community
– Published collections of
data from a range of social
media data sources to help
students and researchers
connect with data of
interest and relevance

29. What we have done: Open Scholarship

30. What we have done: Open Scholarship

31. Facebook networks
http://www.connectedaction.net/2010/04/25/bernie-hogans-facebook-social-network-data-provider-and-visualization-toolkit/

32. Twitter Networks: connections among the people who tweeted the term
“Kpop” on 24 October 2011

33. NodeXL data import sources

34. Example NodeXL data importer for Twitter

35. NodeXL imports “edges” from social media data sources

36. NodeXL Automation makes analysis simple and fast

37. NodeXL Network Metrics

38. NodeXL simplifies mapping data attributes to display attributes

39. NodeXL displays subgraph images along with network metadata

40. NodeXL enables filtering of networks

41. NodeXL Generates Overall Network Metrics

42. What we want to do:
(Build the tools to) map the social web
• Move NodeXL to the web:
– Node for Google Doc Spreadsheets!
– WebGL Canvas
• Connect to more data sources of interest:
– RDF, MediaWikis, Gmail, NYT, Citation Networks
• Solve hard network manipulation UI problems:
– Modal transform, Time series, Automated layouts
• Grow and maintain archives of social media network data sets for
research use.
• Improve network science education:
– Workshops on social media network analysis
– Live lectures and presentations
– Videos and training materials

43. Work Items
Autofill Group Attribute
Merge Edges by Attribute
Modal Transform
Merge Workbooks
Automated Dynamic Filters: Time Series Analysis, contrast
Captions and Legends
Upload to Graph Gallery++: captions, workbook
Graph Gallery++
User Accounts, Reporting, RSS Feeds,
Network Visualization Web Canvas
Import: RDF, Wiki, SharePoint, Keyword networks from text
Metrics: Triad Census
Layouts:
Force Atlas 2, Lin Log, “Bakshy Plots”, Quality Measures
Query-by-example search for network structures

44. How you can help
• Sponsor a feature
• Sponsor Webshop 2012
• Sponsor a student
• Schedule training
• Sponsor the foundation
• Donate your money, code, computation, storage,
bandwidth, data or employee’s time
• Help promote the work of the Social Media
Research Foundation

45. Contact:
Marc A. Smith
Chief Social Scientist
Connected Action Consulting Group
Marc@connectedaction.net
http://www.connectedaction.net
http://www.codeplex.com/nodexl
http://www.twitter.com/marc_smith
http://delicious.com/marc_smith/Paper
http://www.flickr.com/photos/marc_smith
http://www.facebook.com/marc.smith.sociologist
http://www.linkedin.com/in/marcasmith
http://www.slideshare.net/Marc_A_Smith
http://www.smrfoundation.org

46. Charting Collections of
Connections in Social
Media:
Creating Maps and
Measures with NodeXL
A project from the Social Media Research Foundation: http://www.smrfoundation.org