The Basics of Network Computing

1. The Basics of Network Computing Michael T. Heaney University of Michigan August 31, 2011 3-Hour lesson This material is distributed under an Attribution‐NonCommercial‐ShareAlike 3.0 Unported Creative Commons License, the full details of which may be found online here: http://creativecommons.org/licenses/by‐nc‐sa/3.0/ . You may re‐use, edit, or redistribute the content provided that the original source is cited, it is for noncommercial purposes, and provided it is distributed under a similar license.

2. Plan for the Afternoon • Choosing a Network Program • Working with Network Data • Basic network statistics • Visualization

3. Principal Tasks of Network Computing • Visualization of Networks • Calculation of Descriptive Statistics • Advanced Network Analysis (e.g., ERGM) When considering which statistical package to use, consider which of the above tasks your work will focus on.

4. UCINet • Operates well in the familiar windows environment, but may be difficult to use with Apple computers. • Allows calculation of most standard network statistics, but is less adept at handling advanced analysis (e.g., ERGM). • Point-and-click approach is relatively easy to learn, but it can be a bit clunky. • Available here: http://www.analytictech.com/ucinet/download.htm

5. Statnet in R • Operates well in both Windows and Apple computing environments • Performs both basic and advanced network analyses • Users can develop own network analysis routines • Steep learning curve • Available here: http://statnetproject.org/

6. Some Other Packages • MelNet – Specializes in Exponential Random Graph Models. Available: http://www.sna.unimelb.edu.au/ • Pajek – Specializes in large network analysis. Available: http://vlado.fmf.uni-lj.si/pub/networks/pajek/ • SoNIA– Visualizing Dynamic Networks. Available: http://www.stanford.edu/group/sonia/ • And more…..

7. UCINet • A good place to start training even if you are going to shift to another program.

8. Importing Data Simplest approach is to read an Excel file. • Open UCINet • Click on Spreadsheet Icon • File  Open  Excel Files  Filename.xlsx • In this case, open Hrmatrix.xlsx • Save as UCINET 7 dataset • Note the creation of two files filename.##h and filename. ##d – you will need both of these files in order to use UCINET data.

9. Data List Files • A good alternative when you are working with large data sets • Create using a simple text file: dl nr = 1945 nc = 525, format = edgelist2, labels embedded data: 10270716051 Communist 10270716049 UFPJ 10270716048 BrooklynPeace 10270716045 BrooklynPeace 10270716045 UFPJ

11. Read a Data List File • Data Import Text File  DL… Contact_Network_Data  OK

12. More Varied DL Formats for Data • Best to learn this on your own using UCINet help • Help  Help Topics  DL

14. Basic Data Analysis – Density • Network  Cohesion  (new) Density Overall  Hrmatrix

16. Compute Density with Two-Mode Data • Network  2-Mode networks  2-mode Cohesion  Input 2-mode incidence matrix  OK

18. Basic Network Analysis – Centrality • Network  Centrality and Power  Multiple Measures (old)

20. Using Your Centrality Data in Statistical Analysis • Spreadsheet  File  Open  Centrality • Save as type  Excel • Excel  File  Open

21. Compute Centrality with Two-Mode Data • Network  2-Mode Networks  2-Mode Centrality  Input 2-mode matrix  Contact_Network_Data.##h  OK

23. Convert Two-Mode Data to One-Mode Data • Data  Affiliations (2-mode to 1-mode)  Input data  …  Contact_Network_Data  Which mode  Column [for this particular example]

25. Using Your Affiliation Data • Note that your new one-mode data (i.e., affiliation data) has been saved as a new file: Contact_Network_Data-ColAff • You can conduct all network analysis on this dataset • Let’s look at it: • Spreadsheet  File  Open  Contact_Network_Data-ColAff OK • Note that your cells make are counts of affiliations, which is why we call this affiliation data

27. Dichotomizing Data • Are data may be valued, but we may preferred that they be dichotomous • Transform  Dichotomize  Contact_Network_Data-ColAff • Our output will now have only 1s and 0s

29. Basic Visualization • Visualize  Netdraw • File  Open  Ucinet Dataset  Network  Choose File

30. Refine Visualization • Open  Ucinet dataset  Attribute data  HRattributes • Properties  Lines  Arrow Heads  Visible  Off • Properties  Nodes  Symbols  Size  Attribute Based  Age • Properties  Nodes  Symbols  Shape  Attribute Based  English_language • Layout  Graph-Theoretic Layout  Spring Embedding  OK

31. A New View of the Network

32. Visualizing Contact Network Data • UCINet Spreadsheet  File  Open  Excel Files  Hybrid_Variable.xlsx • File  Save As  UCINet 7 dataset  Hybrid_Variable • Visualize  Netdraw • File  Open  Ucinet Dataset  Network Contact_Network_Data-ColAff • File  Open  Ucinet Dataset  Attribute data Hybrid_Variable

34. Visualizing Contact Network Data – Continued • Click on delete isolates buttons • Layout  Graph Theoretic Layout  Spring Embedding (You may need to do this twice) • Analysis  Components  OK

36. Visualizing Contact Network Data – Continued • Click on MC button to look at main component only • Turn off labels, arrow heads • Repeat spring embedding • Properties  Lines  Size  Tie Strength  1 to 10 • Properties  Nodes  Symbols  Shape  Attribute Based  Select Attribute  Hybrid Variable  OK • Click a node  Choose label visible

38. Visualizing Contact Network Data – Continued • Analysis  Subgroups  Factions  2 (or 3 or 4)  Go!

40. Next Steps • Multiplex Visualizations • Three Dimensional Visualizations • Advanced analysis  Exponential Random Graph Models