The Basics of Network Computing

Michael T. Heaney

University of MichiganAugust 31, 2011

3-Hour lessonThis 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.

Plan for the Afternoon

• Choosing a Network Program• Working with Network Data• Basic network statistics• Visualization

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.

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

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/

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…..

UCINet

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

Importing Data

Simplest approach is to read an Excel file.

1. Open UCINet2. Click on Spreadsheet Icon3. File Open Excel Files Filename.xlsx4. In this case, open Hrmatrix.xlsx5. Save as UCINET 7 dataset6. Note the creation of two files filename.##h and

filename. ##d – you will need both of these files in order to use UCINET data.

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 embeddeddata:10270716051Communist10270716049UFPJ10270716048BrooklynPeace10270716045BrooklynPeace10270716045UFPJ

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

More Varied DL Formats for Data

• Best to learn this on your own using UCINet help

• Help Help Topics DL

Basic Data Analysis – Density

• Network Cohesion (new) Density Overall Hrmatrix

Compute Density with Two-Mode Data

• Network 2-Mode networks 2-mode Cohesion Input 2-mode incidence matrix OK

Basic Network Analysis – Centrality

• Network Centrality and Power Multiple Measures (old)

Using Your Centrality Data in Statistical Analysis

• Spreadsheet File Open Centrality• Save as type Excel• Excel File Open

Compute Centrality with Two-Mode Data

• Network 2-Mode Networks 2-Mode Centrality Input 2-mode matrix Contact_Network_Data.##h OK

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]

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

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

Basic Visualization

• Visualize Netdraw• File Open Ucinet Dataset Network

Choose File

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

A New View of the Network

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

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

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

Visualizing Contact Network Data – Continued

• Analysis Subgroups Factions 2 (or 3 or 4) Go!

Next Steps

• Multiplex Visualizations• Three Dimensional Visualizations• Advanced analysis Exponential Random

Graph Models