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3. The NodeXL Project Team

4. 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

5. 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), – betweenness Methods – Surveys, interviews, observations, log file analysis, computational analysis of matrices (Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001) Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser University. pp.7- 16 Social Network Theory http://en.wikipedia.org/wiki/Social_network http://en.wikipedia.org/wiki/Social_network

6. Social Networks History: from the dawn of time! Theory and method: 1934 -> Jacob L. Moreno http://en.wiki pedia.org/wiki /Jacob_L._Mor eno http://en.wiki pedia.org/wiki /Jacob_L._Mor eno

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

8. Two “answer people” with an emerging 3 rd. Mapping Newsgroup Social Ties Microsoft.public.windowsxp.server.general 8

9. Distinguishing attributes of online social roles Answer person – Outward ties to local isolates – Relative absence of triangles – Few intense ties Reply Magnet – Ties from local isolates often inward only – Sparse, few triangles – Few intense ties 9

10. Distinguishing attributes: Answer person – Outward ties to local isolates – Relative absence of triangles – Few intense ties Discussion person – Ties from local isolates often inward only – Dense, many triangles – Numerous intense ties 10

11. Introduction to NodeXL

12. NodeXL: Network Overview, Discovery and Exploration for Excel Leverage spreadsheet for storage of edge and vertex data http://www.codeplex.com/nodexl

14. #WIN09

15. http://www.youtube.com/watch?v=0M3T65Iw3Ac NodeXL Video

16. NodeXL Free/Open Social Network Analysis add-in for Excel 2007 makes graph theory as easy as a bar chart, integrated analysis of social media sources. http://nodexl.codeplex.com

17. Import data from a variety of SNA and Social Media data sources

18. NodeXL Network Metrics

19. A minimal network can illustrate the ways different locations have different values for centrality and degree Diane has high degree Heather has high betweeness NodeXL Network Overview Discovery and Exploration add-in for Excel 2007

20. NodeXL: Display nodes with subgraph images sorted by network attributes using Excel Data|Sort

21. “SAP” mentioning twitter users

22. Size = Followers Edge = # relationship ties

23. The NodeXL project is Available via the CodePlex Open Source Project Hosting Site: http://www.codeplex.com/nodexl

24. Display community members sorted by network attributes using Excel Data|Sort

25. Summary network metrics Displayed on “Overall Metrics” tab

26. Map data to display attributes

27. Dynamic Filters Now feature Metrics histograms

28. Import from flickr tag and user networks

29. Network Clusters visualization showing three Flickr tag clusters, each representing a different context for “mouse”.

30. Isolate clusters showing three different contexts for the “mouse” tag in Flickr: mouse animal, computer mouse, and Mickey Mouse character.

31. NodeXL Network of Flickr users who comment on Marc_Smith’s photos (network depth 1.5; edge weight ≥ 4).

32. Import data from Twitter user and term networks

33. NodeXL Tutorial http://casci.umd.edu/ Book forthcoming: Analyzing social media networks with NodeXL: Insights from a connected world Book forthcoming: Analyzing social media networks with NodeXL: Insights from a connected world

34. Social media network archives On-going collection Additional sources: enterprise/consumer More metrics Performance Cross-platform/Web Clustering Time series analysis

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39. 39 Answer Person Signatures Discussion People

40. Spammer Discussion Starter Reply oriented Discussion Flame Warrior 40