Social Network Analysis Tutorial
Rob Cross
University of Virginia
robcross@virginia.edu
1
Social network analysis tutorial
 Planning and Administering a Network
Analysis
 Visual Analysis of Social Networks
 Quantitative Analysis of Social Networks
2
Planning and administering a network
analysis
Selecting an
Appropriate
Group
Survey
Design
Administering
the Survey
Formatting
Data
3
Social network analysis tutorial
 Planning and Administering a Network Analysis
 Visual Analysis of Social Networks
 Quantitative Analysis of Social Networks
4
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 www.analytictech.com
• NetDraw: Visualization software that creates pictures of
networks. It can also incorporate attribute data into the diagrams.
See www.analytictech.com
• Pajek: Sophisticated visualization software available from
http://vlado.fmf.uni-lj.si
• Mage: Three dimensional drawing tool available from
ftp://152.174.194/pcprograms/Win95_98_2000/
5
An Overview of UCINET
6
Transferring Data from Excel
7
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.
8
Transferring Attribute Data into UCINET
Step 1. Copy data from Excel
Step 2. Paste into spreadsheet editor in UCINET
Step 3. Save as “attrib”
9
Opening Data in NetDraw
Step 1. File > Open > Ucinet dataset > Network
Step 2. Choose network dataset (info.##h)
10
Opening Data in NetDraw
Step 1. Click - open folder icon
Step 2. Click - box
Step 3. Choose network dataset (info.##h), then click OK.
11
Dichotomizing in NetDraw
Step 1. Choose “>=” and “4”
12
Using Drawing Algorithm in NetDraw
Step 1. Choose
Step 2. Choose
option on tool bar
= option on tool bar
13
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.
14
Choosing Color Attribute in NetDraw
Step 1. Select “Nodes”
Step 2. Select “Region”
Step 3. Place a check mark in the color box
15
Selecting Nodes in NetDraw
Step 1. Default is all groups selected. To remove one group, e.g. group 2,
remove check from box
16
Selecting Egonets in NetDraw
Step 1. Layout > Egonets
Step 2. Choose egonet initials, e.g. BM
17
Changing the Size of Nodes in NetDraw
Step 1. Properties > Nodes > Size > Attribute-based
Step 2. Select attribute, e.g. gender
18
Changing the Shape of Nodes in NetDraw
Step 1. Properties > Nodes > Shape > Attribute-based
Step 2. Select attribute, e.g. hierarchy
19
Changing the Size of Lines in NetDraw
Step 1. Properties > Lines > Size > Tie strength
Step 2. Select minimum =1 and maximum = 5
20
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
21
Deleting Isolates in NetDraw
Step 1. Select Iso option on the toolbar
22
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
23
Resizing and Re-centering in NetDraw
Step 1. Layout > Move/Rotate
Step 2. Select “Center” option
24
Saving Pictures in NetDraw
Step 1. File > Save diagram as > Bitmap
Step 2. Choose file name, e.g. “infoge4region”
25
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?”
Network Measures
Density
5%
Cohesion
n/a
Centrality
15
I do not typically seek information from this person
Network Measures
“From whom do you typically give work-related
information?”
Network Measures
Density
5%
Cohesion
n/a
Centrality
15
I do not typically give information to this
Network Measures
Density
5%
Density
4%
Cohesion
2.6
Cohesion
2.6
Centrality
12
Centrality
13
I do typically seek information from this person
I do typically give information to this person
26
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
= Location 1
= Location 2
= Location 3
= Location 4
= Location 5
= Location 6
= Location 7
= Location 8
= Location 9
= Location 10
= Location 11
= Location 12
Network Measures
Density = 3%
Cohesion = 4.0
Centrality = 3.1
27
Social network analysis tutorial
 Planning and Administering a Network Analysis
 Visual Analysis of Social Networks
 Quantitative Analysis of Social Networks
28
Quantitative Analysis of Organizational
Networks
Measures
of Network
Connection
Measures of
Centrality
Cross
Boundary
Analysis
29
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 3. Choose cut-off op. and value (e.g. GE and 4)
Step 1. Transform > Dichotomize
Step 2. Choose input dataset (info.##h) Step 4. Specify output data set (infoGE4.##h)
30
Measures of Network
Connection
Network
Connection
Centrality
Cross
Boundary
Analysis
 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.
31
Density
Low Density (25%)
Avg. Dist. = 2.27
Network
Connection
Centrality
Cross
Boundary
Analysis
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
32
Quantitative Analysis:
Density
Network
Connection
Centrality
Cross
Boundary
Analysis
Density of this network is 8%.
Step 1. Network > Cohesion > Density
Step 2. Input dataset “infoge4.##h”
33
Distance
Short average distance
Network
Connection
Centrality
Cross
Boundary
Analysis
Long average distance
 Average number of steps to reach all network participants
 Lower scores reflect a group better able to leverage knowledge
34
Quantitative Analysis:
Distance
Network
Connection
Centrality
Cross
Boundary
Analysis
Average Distance is 3.5
Step 1. Network > Cohesion > Distance
Step 2. Input dataset “infoge4.##h”
35
Measures of Centrality
Network
Connection
Centrality
Cross
Boundary
Analysis
 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.
36
Degree Centrality
Network
Connection
Centrality
Cross
Boundary
Analysis
y
x
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
37
Closeness Centrality
Network
Connection
Centrality
Cross
Boundary
Analysis
c
a
f
i
h
d
j
b
g
e
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
38
Betweenness Centrality
Network
Connection
Centrality
Cross
Boundary
Analysis
c
a
f
k
l
j
m
h
d
b
g
e
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
39
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
40
Pulling People Dynamically From the
Network…
41
Quantitative Analysis:
Degree Centrality
Network
Connection
Centrality
Cross
Boundary
Analysis
Step 1. Network > Centrality > Degree
42
Quantitative Analysis:
Degree centrality
Network
Connection
Centrality
Cross
Boundary
Analysis
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.
43
Quantitative Analysis:
Degree Centrality
Network
Connection
Centrality
Cross
Boundary
Analysis
In-degree for HA is 7
44
Quantitative Analysis:
Degree Centrality
Network
Connection
Centrality
Cross
Boundary
Analysis
Average in-degree is 3.7
In-degree Network
Centralization is 12%
45
Opportunities exist to re-distribute relational load. Focus on ways to delayer 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?”
# People Receives Information From
90.00
80.00
Integrators
High Info
Sources
70.00
60.00
279
163
78
170
117
295
50.00
196
37
93
272
40.00
90
255
53
275
119
30.00
278
263
6
171
26
201
141
248177
5161
273
54299
8
300
178
19722
233
118
9
212
16
82
52
229 211
55
203
308
174
113
184 158 199
7 249
20.00 135
268
3
147 140
294
270
133
28
303
175
243
169
95 81
224 69 241
127
30
286
245189
126
191 105
202
45
265 230 14
198 35
217
234
132
39
3874
5 220
301
240
59
36
221
24
296
143 164
315
100
231 183
75 144 87
19
29
155 48
10.00
32
302
195
292
216
27256
99
60
205
242
190
101
269
57131
56 185
153
23
102
148176
210
92
91
264
258 1
213
257
317
89
237
47192
44 167
246
15 244
188
222
2106
316
10 209
312
149
223
206
120
43
280
34 314
139
116
281
193
247
67
111
50 276
145
311
136
112
239
160
266
173
187
High Info
Seekers
0.00
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
# People Each Person Seeks Information From
* Calculations based on people who responded to the survey only
46
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
# People Receives Information From
50
Integrators
High Info Sources
40
BKA/BA/Research Analyst
Assoc/Know. Assoc
Speialist/Sr. Spec
30
M anager
EM /PKM
Assoc Principal
20
Partner
External
Admin/Assistant
10
High Info Seekers
0
0
10
20
30
40
50
# People Each Person gives Information To
47
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
48
Showing performance implications can quickly
get people’s attention…
HelpOut HelpIn
KnowOut KnowIn
KnbefOut knbefin
SocOut SocIn
Sat
10
13
36
30
34
30
25
24
10
14
16
32
26
24
27
35
0
2
6
4
3
1
6
5
1
6
17
26
22
22
15
17
0
3
10
6
4
6
0
3
12
5
31
16
22
18
22
19
0
5
3
19
23
26
3
12
3
6
28
30
11
15
25
25
5
8
14
19
12
15
16
19
16
20
30
39
34
34
38
37
8
10
34
36
29
29
19
29
19
15
42
35
40
37
22
22
7
10
33
31
22
21
34
34
53
31
38
37
34
33
22
28
13
8
34
29
10
7
34
30
23
18
38
34
27
28
29
28
9
9
26
19
14
14
28
23
11
13
39
31
15
18
43
36
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
5
5
49
Cross-boundary Analysis
Network
Connection
Centrality
Cross
Boundary
Analysis

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.
50
Cross-boundary Analysis
Network
Connection
Centrality
Cross
Boundary
Analysis
Information Network: Density as related to practice
Please indicate how often you have turned to this person for information or advice on workrelated topics in the past three months (response of often or very often).
Healthcare
Government
IT
Oil & Gas
Pharmaceuticals
Industrial
Healthcare Government
17%
0%
0%
17%
0%
0%
4%
0%
35%
0%
1%
9%
IT
0%
0%
0%
0%
0%
9%
Oil & Gas Pharmaceuticals Industrial
7%
38%
0%
0%
0%
10%
0%
0%
6%
19%
3%
8%
1%
49%
0%
12%
1%
8%
51
Density Across Practice
Network
Connection
Centrality
Cross
Boundary
Analysis
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
52
Broker Categories
Network
Connection
Centrality
Cross
Boundary
Analysis
Ego
Coordinator - This person connects people within their group.
A
Ego
Gatekeeper - This person is a buffer between their own group
and outsiders. Influential in information entering the group.
B
A
Ego
Representative - This person conveys information from their
group to outsiders. Influential in information sharing.
B
A
B
53
Quantitative Analysis:
Broker Metrics
Network
Connection
Centrality
Cross
Boundary
Analysis
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”
54
Additional Quantitative Analysis
 Symmetrization & Verification
 Scatter Plots
 Combining Networks
 QAP Correlation and Regression
55
Symmetrizing Data
Bill


John
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)
56
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”
57
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
58
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”
59
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
60
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.
61
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)”
62
QAP Correlation
Step 1. Tools > Testing Hypothesis > Dyadic (QAP) > QAP Correlations
Step 2. 1st Data Matrix “InfoGE4”
Step 3. 2nd Data Matrix “ValueGE4”
63
QAP Regression
Adjusted R-Square of 0.214 indicates a moderate
relationship between the two social relations. The
probability of 0.000 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”
64