UCINET Visualization and Quantitative Analysis Tutorial

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UCINET Visualization and

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Session 1 – Network Visualization

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Transferring Data from Excel

(From Tab ConCoInfo)

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Transferring Excel Matrix Data into UCINET

Step 1. Copy data from Excel

Step 2. Open spreadsheet editor in UCINET Step 3. Paste into spreadsheet editor in UCINET Step 4. Save as “info”

Button To Open Spreadsheet Editor

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Transferring Attribute Data into UCINET

(From Tab: ConcoAttr)

Step 1. Copy data from Excel

Step 2. Open spreadsheet editor in UCINET Step 3. Paste into spreadsheet editor in UCINET Step 4. Save as “attrib”

Button To Open Spreadsheet Editor

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Opening NetDraw For Visualization

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Opening Data in NetDraw

Step 1. File > Open > Ucinet dataset > Network Step 2. Choose network dataset (info.##h)

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Opening Data in NetDraw

Step 1. Click - open folder icon

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Dichotomizing in NetDraw

Step 1. Click Relations Tab

Step 2. Select “Greater Than” Operator

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Using Drawing Algorithm in NetDraw

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Using Attribute Data in NetDraw

Step 1. Click - open folder icon

Step 2. Choose attribute dataset (attrib.##h), then click Open.

Step 3. Click “OK” On Matching Box And “X” Out Of Attribute Editor.

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Choosing Color Attribute in NetDraw

Step 1. Select “Nodes” Step 2. Select “Region”

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Selecting Nodes in NetDraw

Step 1. Default is all groups selected. To remove one group, e.g. group 2, remove check from box

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Selecting Egonets in NetDraw

Step 1. Select “Ego” Button On ToolBar

Step 2. Ensure Geodesic distance FROM/TO ego is <= 1 Step 3. Select “BM”

Step 4. De-Select “AR”

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Changing the Size of Nodes in NetDraw

Step 1. Properties > Nodes > Symbols > Size > Attribute-based

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Changing the Shape of Nodes in NetDraw

Step 1. Properties > Nodes > Symbols > Shape > Attribute-based Step 2. Select attribute, e.g. hierarchy

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Changing the Size of Lines in NetDraw

Step 1. Properties > Lines > Size > Tie strength Step 2. Select minimum =1 and maximum = 5

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Changing the Color of Lines in NetDraw

Step 1. Properties > Lines > Color > Node attribute-based

Step 2. Select Region attribute, then choose within, between or both

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Deleting Isolates in NetDraw

Step 1. Select Iso option on the toolbar

Step 2. Select ~Nodes button to bring back removed nodes (click on “Okay” in pop-up box)

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Resizing and Re-centering in NetDraw

Step 1. Layout > Move/Rotate Step 2. Select “Center” option

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Saving Pictures in NetDraw

Step 1. File > Save diagram as > Jpeg

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Session 1 – Network Visualization

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 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.

Dichotomizing Valued Data

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 (Info_GE_4)

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Measures of Network Connection

• 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.

Network

Connection Centrality

Cross Boundary Analysis

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Density

• Number of ties, expressed as percentage of the number of pairs

• Dense networks have more face-to-face relationships

Low Density (25%)

Avg. Dist. = 2.27 High Density (39%) Avg. Dist. = 1.76

Network

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Quantitative Analysis: Density

Step 1. Network > Cohesion > Density > Density Overall Step 2. Input dataset “Info_GE_4”

Network

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Distance

 Average number of steps to reach all network participants

 Lower scores reflect a group better able to leverage knowledge

Short average distance

Long average distance

Network

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Quantitative Analysis: Distance

Step 1. Network > Cohesion > Geodesic Distance (old) Step 2. Input dataset “Info_GE_4”

Network

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Measures of 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.

Network

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Quantitative Analysis: Degree Centrality

Step 1. Network > Centrality and Power > Degree

Network

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Quantitative Analysis: Degree Centrality

Step 1. Input dataset “Info_GE_4”

Step 2. Choose whether to treat data as symmetric. I almost always select no. If you choose “no” it will calculate separate figures for the people you go to and the people that come to you.

Network

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Quantitative Analysis: Degree Centrality

Network Connection Centrality

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Quantitative Analysis: Degree Centrality

Network Connection Centrality

Average in-degree is 3.652

In-degree Network

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0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00

175 302 111 279 105 308 47 263 90 273 37 51 276 300 176 15 22 240 177 160 139 101 43 74 316 234 30 117 231 192 143 57₂₅₈ 81 312 205 257 195 188 255 315 292 173 99 2 256 224 178 106 241 75 113 246 149 145 116 78 191 140 222 202 118 242 193 54 296 89 102 148 19 6 248 32 35 295 230 270 91 223 201 45 3 198 163 164 209 167 217 38 93 20634 61 174 211 303 112 144 265 1 187 7 69 212 155 5 299 10 189 26 247 16

27 153 216

243 268 95 147 23 237 170 301 311 266 249 119 28 52 29 92 169 100 82 120₅₀ 269 280 221 278 59 210 141 60 132 239 55 171 36 294 245 229 185 48 39 220 275 131 233 9 ₁₈₄ 56 67 8 135 136 24 213 190 196 127 158 264 286 272 183 133 281 197 203 199 44 53 87 244 14 314 317 126

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

# People Each Person Seeks Information From

#

Peopl

e

Rec

eiv

es

Infor

m

at

ion F

rom

High Info Sources High Info Seekers Integrators

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ScatterPlot Step 1: Save Text File

Step 1. Generate Degree Calc. Network > Centrality > Degree > Info_GE_4 Step 2. File > Save As > Degree Output Text

Network

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ScatterPlot Step 2: Save Text File

Step 1. Open Excel

Step 2. File > Open > Txt > Degree Output Text Step 3. Step 1 (In Text Import Wizard) > Next

Step 4. Step 2 (Pictured) > Insert De-Limiter Between Names and Number. Step 5. Step 3 Finish

Network

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ScatterPlot Step 3: Insert Columns

Back In UCINET

Step 1. Open UCINET Spreadsheet Editor

Step 2. Cut And Paste Relevant Headers And In/Out Degree Numbers Step 3. Save As A UCINET file titled, “Scatterplot”

Network

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ScatterPlot Step 4:

Create Plot In UCINET

Step 1. Tools > Scatterplot

Step 2. Click on open file folder to open “Scatterplot” Step 3. Play with options (e.g., uniform axis)

Network

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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. Network Connection Centrality Cross Boundary Analysis

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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 work-related topics in the past three months (response of often or very often).

Healthcare Government IT Oil & Gas Pharmaceuticals Industrial

Healthcare 17% 0% 0% 7% 38% 0%

Government 0% 17% 0% 0% 0% 10%

IT 0% 0% 0% 0% 0% 6%

Oil & Gas 4% 0% 0% 19% 3% 8%

Pharmaceuticals 35% 0% 0% 1% 49% 0%

Industrial 1% 9% 9% 12% 1% 8%

Network

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Density Across Practice

Step 1. Network > Cohesion > Density > Old Density Procedure Step 2. Input dataset “Info_GE_4”

Step 3. Click on “…” to select “Attrib” file for Row Partitioning. Arrow to end to select col 3.

Step 4. Column Partitioning will automatically be filled in with the same text as the Row Partition. Step 5. Scroll all the way down in output file for density matrix.

Network

Tip: Col 3 is the column that includes the practice attribute. You can select different columns for different attributes MAKE SURE TO USE THE “DENSITY /

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Broker Categories

Coordinator - This person connects people within their group. Ego

A B

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.

B Ego A Network Connection Centrality Cross Boundary Analysis

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Quantitative Analysis: Broker Metrics

Step 1. Network > Ego networks > G&F Brokerage Step 2. Input dataset “Info_GE_4”

Step 3. Partition vector “attrib col 2”

Tip: Col 2 is the column that includes the gender attribute. You can select different columns for different attributes

Network

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Additional Quantitative Analysis

• Symmetrization & Verification

• Combining Networks

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Symmetrizing Data

• 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)

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Symmetrizing Data (Continued)

Step 1. Transform > Symmetrize Step 2. Input dataset “Info_GE_4”

Step 3. Symmetrizing method “maximum” Step 4. Output dataset “Info_GE_4-Sym”

Tip: See previous slide for how to choose the most applicable

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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.

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Combining Networks (Continued)

Step 1. Tools > Matrix Algebra

Step 2. In the Enter Command box type “infovalue = mult(ArtCoInfo_GE_4,ArtCoKase)”

Tip: The new matrix “infovalue” can now be used for various visual and quantitative analysis.

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QAP Correlation

Step 1. Tools > Testing Hypothesis > Dyadic (QAP) > QAP Correlation (old) Step 2. 1st Data Matrix “ArtCoInfo_GE_4”

Step 3. 2nd Data Matrix “ArtCoKase” (note that this file is already 1’s and 0’s so no need to dichotomize)

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QAP Regression

Step 1. Tools > Testing Hypothesis > Dyadic (QAP) > MR-QAP Linear Regression > Original (Y-permutation) method

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QAP Regression (cont.)

Step 1. Enter dependent variable “ArtCoInfo_GE_4” Step 2. Enter independent variable “ArtCoKASE”

Adjusted R-Square of 0.133 indicates a moderate relationship between the two social relations. The