1. Social Network Analysis Tutorial
Rob Cross
University of Virginia

2. Social network analysis tutorial
Planning and Administering a Network Analysis
Visual Analysis of Social Networks
Quantitative Analysis of Social Networks

3. Planning and administering a network analysis
Formatting
Data
Administering
the Survey
Survey
Design
Selecting an
Appropriate
Group

4. Social network analysis tutorial
Planning and Administering a Network Analysis
Visual Analysis of Social Networks
Quantitative Analysis of Social Networks

5. Organizational Network Analysis Software
There are numerous network analysis software packages available. We use the following.
UCINET: Windows based tool which is used to manipulate and analyze the data. It includes a comprehensive range of network techniques. See
NetDraw: Visualization software that creates pictures of networks. It can also incorporate attribute data into the diagrams. See
Pajek: Sophisticated visualization software available from
Mage: Three dimensional drawing tool available from ftp:// /pcprograms/Win95_98_2000/

6. An Overview of UCINET

7. Transferring Data from Excel

8. Transferring Excel Matrix Data into UCINET
Step 1. Copy data from Excel
Step 2. Paste into spreadsheet editor in UCINET
Step 3. Save as “info,” etc.

9. Transferring Attribute Data into UCINET
Step 1. Copy data from Excel
Step 2. Paste into spreadsheet editor in UCINET
Step 3. Save as “attrib”

10. Opening Data in NetDraw
Step 1. File > Open > Ucinet dataset > Network
Step 2. Choose network dataset (info.##h)

11. Opening Data in NetDraw
Step 1. Click - open folder icon
Step 2. Click - box
Step 3. Choose network dataset (info.##h), then click OK.

12. Dichotomizing in NetDraw
Step 1. Choose “>=” and “4”

13. Using Drawing Algorithm in NetDraw
Step 1. Choose option on tool bar
Step 2. Choose = option on tool bar

14. Using Attribute Data in NetDraw
Step 1. Click - open folder icon A
Step 2. Click - box
Step 3. Choose attribute dataset (attrib.##h), then click OK.

15. Choosing Color Attribute in NetDraw
Step 1. Select “Nodes”
Step 2. Select “Region”
Step 3. Place a check mark in the color box

16. Selecting Nodes in NetDraw
Step 1. Default is all groups selected. To remove one group, e.g. group 2,
remove check from box

17. Selecting Egonets in NetDraw
Step 1. Layout > Egonets
Step 2. Choose egonet initials, e.g. BM

18. Changing the Size of Nodes in NetDraw
Step 1. Properties > Nodes > Size > Attribute-based
Step 2. Select attribute, e.g. gender

19. Changing the Shape of Nodes in NetDraw
Step 1. Properties > Nodes > Shape > Attribute-based
Step 2. Select attribute, e.g. hierarchy

20. Changing the Size of Lines in NetDraw
Step 1. Properties > Lines > Size > Tie strength
Step 2. Select minimum =1 and maximum = 5

21. Changing the Color of Lines in NetDraw
Step 1. Properties > Lines > Color > Node attribute-based
Step 2. Select attribute, then choose within, between or both

22. Deleting Isolates in NetDraw
Step 1. Select Iso option on the toolbar

23. Combining Relations in NetDraw
Step 1. Properties > Lines > Boolean selection
Step 2. Select relations, e.g. info and value
Step 3. Select cut-off operators and values, e.g. >= 4

24. Resizing and Re-centering in NetDraw
Step 1. Layout > Move/Rotate
Step 2. Select “Center” option

25. Saving Pictures in NetDraw
Step 1. File > Save diagram as > Bitmap
Step 2. Choose file name, e.g. “infoge4region”

26. The information seeking and information giving networks are both loosely connected. This represents an opportunity to improve knowledge re-use and leverage throughout the group.
“From whom do you typically seek work-related information?”
“From whom do you typically give work-related information?”
Network Measures
Network Measures
Density 5%
Cohesion
n/a
Centrality 15
Density 5%
Cohesion
n/a
Centrality 15
I do not typically seek information from this person
 I do not typically give information to this
Network Measures
Network Measures
Density 5%
Cohesion
2.6
Centrality 12
Density 4%
Cohesion
2.6
Centrality 13
I do typically seek information from this person
 I do typically give information to this person

27. Visual Data Display: Packing info in and allowing time for interpretation…
Information: “How often do you typically turn to this person for information to get your work done? Network includes responses to this statement of often to continuously (4,5&6).
= Location 2
= Location 1
= Location 3
= Location 4
Location
= Location 5
= Location 6
= Location 8
= Location 7
= Location 9
= Location 10
= Location 11
= Location 12
Network Measures
Density = 3%
Cohesion = 4.0
Centrality = 3.1

28. Social network analysis tutorial
Planning and Administering a Network Analysis
Visual Analysis of Social Networks
Quantitative Analysis of Social Networks

29. Quantitative Analysis of Organizational Networks
Measures
of Network
Connection
Cross
Boundary
Analysis
Measures of
Centrality

30. Dichotomizing Valued Data
The survey data that we collect is usually valued data. Although we can use valued data in UCINET we prefer to take different cuts of the data. For example, we may want to examine the data where people only responded “strongly agree” to a question. To do this we dichotomize the data i.e. convert it to zeros and ones where one means strongly agree and zero means any other response.
Step 1. Transform > Dichotomize
Step 2. Choose input dataset (info.##h)
Step 3. Choose cut-off op. and value (e.g. GE and 4)
Step 4. Specify output data set (infoGE4.##h)

31. Measures of Network Connection
Cross
Boundary Analysis
Centrality
Density
Shows overall level of connection within a network.
We can also look at ties within and between groups.
Distance
Shows average distance for people to get to all other people.
Shorter distances mean faster, more certain, more accurate transmission / sharing.

32. Density Number of ties, expressed as percentage of the number of pairs
Network Connection
Cross
Boundary Analysis
Centrality
Low Density (25%)
Avg. Dist. = 2.27
High Density (39%)
Avg. Dist. = 1.76
Number of ties, expressed as percentage of the number of pairs
Dense networks have more face-to-face relationships

33. Quantitative Analysis: Density
Network Connection
Cross
Boundary Analysis
Centrality
Density of this network is 8%.
Step 1. Network > Cohesion > Density
Step 2. Input dataset “infoge4.##h”

34. Distance Short average distance Long average distance
Network Connection
Cross
Boundary Analysis
Centrality
Short average distance
Long average distance
Average number of steps to reach all network participants
Lower scores reflect a group better able to leverage knowledge

35. Quantitative Analysis: Distance
Network Connection
Cross
Boundary Analysis
Centrality
Average Distance is 3.5
Step 1. Network > Cohesion > Distance
Step 2. Input dataset “infoge4.##h”

36. Measures of Centrality
Network Connection
Cross
Boundary Analysis
Centrality
Degree Centrality: How well connected each individual is.
Betweenness Centrality: Extent to which individuals lie along short paths.
Closeness Centrality: How far a person is from all others in the network.

37. Communication Network
Degree Centrality
Network Connection
Cross
Boundary Analysis
Centrality
Communication Network
degree of X is 7
Seek Advice Network
in-degree of Y is 5
How well connected each individual is
Technical definition: Number of ties a person has

38. Closeness Centrality Closeness of F is 13
Network Connection
Cross
Boundary Analysis
Centrality
Closeness of F is 13
How far a person is from all others in the network
Index of how quickly information can flow to that person
Technical definition: Total number of links along shortest paths from the individual to each other individual

39. Betweenness Centrality
Network Connection
Cross
Boundary Analysis
Centrality
Betweenness of h is 28.33
Extent to which individuals lie along short paths
Index of potential to play brokerage, liaison or gatekeeping
Technical definition: number of times that a person lies along the shortest path between two others, adjusted for number of alternative shortest paths

40. Without 12 central people
Without the twelve most central people the network is 26% less well connected, reflecting a vulnerability in the group
“From whom do you typically seek work-related information?”
Network Measures
Density = 5%
Cohesion = 2.6
Centrality = 12
Without 12 central people
Network Measures
Density = 3%
Cohesion = 2.8
Centrality = 9
Responses of I do typically seek information from this person

41. Pulling People Dynamically From the Network…

42. Quantitative Analysis: Degree Centrality
Network Connection
Cross
Boundary Analysis
Centrality
Step 1. Network > Centrality > Degree

43. Quantitative Analysis: Degree centrality
Network Connection
Cross
Boundary Analysis
Centrality
Step 2. Input dataset “infoge4.##h”
Step 3. Choose whether to treat data as symmetric. If you choose “no” it will calculate
separate figures for the people you go to and the people that go to you.

44. Quantitative Analysis: Degree Centrality
Network Connection
Cross
Boundary Analysis
Centrality
In-degree for HA is 7

45. Quantitative Analysis: Degree Centrality
Network Connection
Cross
Boundary Analysis
Centrality
Average in-degree is 3.7
In-degree Network
Centralization is 12%

46. # People Receives Information From
Opportunities exist to re-distribute relational load. Focus on ways to de-layer those in the top right quadrant (info access, decision rights, role) while also better leveraging those in the bottom quadrant
“From whom do you typically seek work-related information?”
Integrators
High Info Sources
# People Receives Information From
High Info Seekers
# People Each Person Seeks Information From
* Calculations based on people who responded to the survey only

47. # People Receives Information From
Opportunities exist to re-distribute relational load. Focus on ways to de-layer those in the top quadrant (info access, decision rights, role) while also better leveraging those in the bottom quadrant
High Info Sources
Integrators
# People Receives Information From
High Info Seekers
# People Each Person gives Information To

48. Predicting Satisfaction
Social Network
Level of Satisfaction: Neutral Satisfied Very Satisfied
There is a statistically significant relationship between Social OutDegree and Level of Satisfaction. (0.022)
Correlation: 0.375

49. Showing performance implications can quickly get people’s attention…

50. Cross-boundary Analysis
Network Connection
Cross
Boundary Analysis
Centrality
Density across boundaries: How connected are groups within themselves and with other pre-defined groups. This view can be used for different boundaries. We have used the following in our research:
Function or other designation of skill or knowledge.
Geographic location (even if only different floors).
Hierarchical level.
Time in organization or time in department.
Personality traits.
Gender (interesting though may be inflammatory).
Brokers: Which individuals are the links between other groups. Brokers can be beneficial conduits of information but they can also hold up the flow of information.

51. Cross-boundary Analysis
Network Connection
Cross
Boundary Analysis
Centrality
Information Network: Density as related to practice
Please indicate how often you have turned to this person for information or advice on work-related topics in the past three months (response of often or very often).

52. Density Across Practice
Network Connection
Cross
Boundary Analysis
Centrality
Tip: Col 3 is the column that includes
the practice attribute. You can select
different columns for different attributes
Step 1. Network > Cohesion > Density
Step 2. Input dataset “infoge4.##h”
Step 3. Row Partitioning “Attrib col 3
Step 4. Column Partitioning “Attrib col 3

53. Broker Categories
Network Connection
Cross
Boundary Analysis
Centrality
Ego
A B
Coordinator - This person connects people within their group.
Gatekeeper - This person is a buffer between their own group
and outsiders. Influential in information entering the group. A
Ego B
Representative - This person conveys information from their
group to outsiders. Influential in information sharing.
Ego A B

54. Quantitative Analysis: Broker Metrics
Network Connection
Cross
Boundary Analysis
Centrality
Tip: Col 2 is the column that includes
the gender attribute. You can select
different columns for different attributes
Step 1. Network > Ego networks > Brokerage
Step 2. Input dataset “infoge4.##h”
Step 3. Partition vector “attrib col 2”

55. Additional Quantitative Analysis
Symmetrization & Verification
Scatter Plots
Combining Networks
QAP Correlation and Regression

56. Symmetrizing Data John Bill
Bill says he communicated with John last week, but John doesn’t mention communicating with Bill
Three options
take the conservative option, and put no tie between John and Bill (minimum)
take the liberal option, and put a tie between John and Bill (maximum)
take the average, assigning a tie strength of 0.5 for the relationship between John and Bill (average)

57. Symmetrizing Data (Continued)
Tip: See previous slide for how to
choose the most applicable
symmetrizing method.
Step 1. Transform > Symmetrize
Step 2. Input dataset “infoge4.##h”
Step 3. Symmetrizing method “maximum”
Step 4. Output dataset “Syminfoge4.##h”

58. Verification of Asymmetric Data
You have both “Give information to” and “Get information from” networks
If A says they give info to B, then B must say that they get info from A
Tip: The new matrix “newinfo” can
now be used for various visual and quantitative
analysis.
Step 1. Tools > Matrix algebra
Step 2. In the Enter Command box type
“newinfo = average(transpose(infofrom),infoto)”
Step 3. Enter

59. Scatterplots
Step 1. Create attribute file spreadsheet editor in UCINET. Each column is taken
from the In-degree numbers in the Degree Centrality function.
Step 2. Save as “Indegree”

60. Scatterplots (Continued)
Step 1. Tools > Scatterplot
Step 2. File name “Indegree”
Step 3. Choose X and Y axis
Step 4. To move initials – point and click
Step 5. To save - File > Save as

61. Combining Networks
In the picture to the left you can see the information network.
In the picture below is the combined information and value network.

62. Combining Networks (Continued)
Tip: The new matrix “infovalue” can now be
used for various visual and quantitative
analysis.
Step 1. Tools > Matrix Algebra
Step 2. In the Enter Command box type “infovalue = mult(infoge4,valuege4)”

63. QAP Correlation
Step 1. Tools > Testing Hypothesis > Dyadic (QAP) > QAP Correlations
Step 2. 1st Data Matrix “InfoGE4”
Step 3. 2nd Data Matrix “ValueGE4”

64. QAP Regression
Adjusted R-Square of indicates a moderate
relationship between the two social relations. The
probability of indicates that it is statistically
significant.
Step 1. Tools > Testing Hypothesis > Dyadic (QAP) > QAP Regression > Original (Y-permutation) method
Step 2. Dependent variable “InfoGE4”
Step 3. Independent variable “ValueGE4”