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1 Social Network Analysis Tutorial Rob Cross University of Virginia [email protected]

1 Social Network Analysis Tutorial Rob Cross University of Virginia [email protected]

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Page 1: 1 Social Network Analysis Tutorial Rob Cross University of Virginia robcross@virginia.edu

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Social Network Analysis Tutorial

Rob CrossUniversity of Virginia

[email protected]

Page 2: 1 Social Network Analysis Tutorial Rob Cross University of Virginia robcross@virginia.edu

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Social network analysis tutorial

Planning and Administering a Network Analysis

Visual Analysis of Social Networks

Quantitative Analysis of Social Networks

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Planning and administering a network analysis

Formatting Data

Administering the Survey

Survey Design

Selecting an Appropriate Group

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Social network analysis tutorial

Planning and Administering a Network Analysis

Visual Analysis of Social Networks

Quantitative Analysis of Social Networks

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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/

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An Overview of UCINET

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Transferring Data from Excel

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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.

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Transferring Attribute Data into UCINET

Step 1. Copy data from Excel

Step 2. Paste into spreadsheet editor in UCINET

Step 3. Save as “attrib”

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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 iconStep 2. Click - boxStep 3. Choose network dataset (info.##h), then click OK.

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Dichotomizing in NetDraw

Step 1. Choose “>=” and “4”

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Using Drawing Algorithm in NetDraw

Step 1. Choose option on tool bar

Step 2. Choose = option on tool bar

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Using Attribute Data in NetDraw

Step 1. Click - open folder icon AStep 2. Click - boxStep 3. Choose attribute dataset (attrib.##h), then click OK.

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Choosing Color Attribute in NetDraw

Step 1. Select “Nodes” Step 2. Select “Region”Step 3. Place a check mark in the color box

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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. Layout > Egonets

Step 2. Choose egonet initials, e.g. BM

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Changing the Size of Nodes in NetDraw

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

Step 2. Select attribute, e.g. gender

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Changing the Shape of Nodes in NetDraw

Step 1. Properties > Nodes > 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 attribute, then choose within, between or both

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Deleting Isolates in NetDraw

Step 1. Select Iso option on the toolbar

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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

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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 > Bitmap

Step 2. Choose file name, e.g. “infoge4region”

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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.

I do typically seek information from this person

Density 5%

Cohesion n/a

Centrality 15

Density 5%

Cohesion 2.6

Centrality 12

Density 4%

Cohesion 2.6

Centrality 13

Density 5%

Cohesion n/a

Centrality 15

Network Measures Network Measures

Network Measures Network Measures

“From whom do you typically seek work-related information?”

I do not typically seek information from this person

“From whom do you typically give work-related information?”

I do typically give information to this person

I do not typically give information to this

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Network Measures

Density = 3%Cohesion = 4.0Centrality = 3.1

= 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

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).

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Social network analysis tutorial

Planning and Administering a Network Analysis

Visual Analysis of Social Networks

Quantitative Analysis of Social Networks

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Quantitative Analysis of Organizational Networks

Cross BoundaryAnalysis

Measures of Centrality

Measures of NetworkConnection

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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 (infoGE4.##h)

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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 Connection Centrality

Cross Boundary Analysis

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Quantitative Analysis: Density

Step 1. Network > Cohesion > DensityStep 2. Input dataset “infoge4.##h”

Density of this network is 8%. Density of this network is 8%.

Network Connection Centrality

Cross Boundary Analysis

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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 Connection Centrality

Cross Boundary Analysis

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Quantitative Analysis: Distance

Step 1. Network > Cohesion > DistanceStep 2. Input dataset “infoge4.##h”

Average Distance is 3.5 Average Distance is 3.5

Network Connection Centrality

Cross Boundary Analysis

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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 Connection Centrality

Cross Boundary Analysis

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Degree Centrality

x

How well connected each individual is Technical definition: Number of ties a person has

y

Communication Networkdegree of X is 7

Seek Advice Networkin-degree of Y is 5

Network Connection Centrality

Cross Boundary Analysis

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Closeness Centrality

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

c

a f

d

b

e

g

h

ij

Closeness of F is 13

Network Connection Centrality

Cross Boundary Analysis

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Betweenness Centrality

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

c

a f

d

b

e

g

h

j

k

m

l

Betweenness of h is 28.33

Network Connection Centrality

Cross Boundary Analysis

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Without the twelve most central people the network is 26% less well connected, reflecting a vulnerability in the group

Network Measures

Density = 5%Cohesion = 2.6Centrality = 12

Network Measures

Density = 3%Cohesion = 2.8Centrality = 9

Without 12 central people

“From whom do you typically seek work-related information?”

Responses of I do typically seek information from this person

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Pulling People Dynamically From the Network…

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Quantitative Analysis: Degree Centrality

Step 1. Network > Centrality > Degree

Network Connection Centrality

Cross Boundary Analysis

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Quantitative Analysis: Degree centrality

Step 2. Input dataset “infoge4.##h”Step 3. Choose whether to treat data as symmetric. If you choose “no” it will calculateseparate figures for the people you go to and the people that go to you.

Network Connection Centrality

Cross Boundary Analysis

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Quantitative Analysis:Degree Centrality

In-degree for HA is 7In-degree for HA is 7

Network Connection Centrality

Cross Boundary Analysis

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Quantitative Analysis: Degree Centrality

Average in-degree is 3.7Average in-degree is 3.7

In-degree NetworkCentralization is 12%

In-degree NetworkCentralization is 12%

Network Connection Centrality

Cross Boundary Analysis

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0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00

175

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111

279

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308

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273

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314317

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

# Pe

ople

Rec

eive

s In

form

atio

n Fr

om

High Info Sources

High Info Seekers

Integrators

“From whom do you typically seek work-related information?”

* Calculations based on people who responded to the survey only

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0

10

20

30

40

50

0 10 20 30 40 50

BKA/BA/Research Analyst

Assoc/Know. Assoc

Speialist/Sr. Spec

Manager

EM/PKM

Assoc Principal

Partner

External

Admin/Assistant

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 Each Person gives Information To

# Pe

ople

Rec

eive

s In

form

atio

n Fr

om

High Info Sources

High Info Seekers

Integrators

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Predicting Satisfaction

Social Network Level of Satisfaction:NeutralSatisfiedVery Satisfied

• There is a statistically significant relationship between Social OutDegree and Level of Satisfaction. (0.022)

• Correlation: 0.375

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Showing performance implications can quickly get people’s attention…

HelpOut HelpIn KnowOut KnowIn KnbefOut knbefin SocOut SocIn Sat10 13 36 30 34 30 25 24 310 14 16 32 26 24 27 35 30 2 6 4 3 1 6 5 31 6 17 26 22 22 15 17 30 3 10 6 4 6 0 3 3

12 5 31 16 22 18 22 19 40 5 3 19 23 26 3 12 43 6 28 30 11 15 25 25 45 8 14 19 12 15 16 19 4

16 20 30 39 34 34 38 37 48 10 34 36 29 29 19 29 4

19 15 42 35 40 37 22 22 47 10 33 31 22 21 34 34 4

53 31 38 37 34 33 22 28 413 8 34 29 10 7 34 30 423 18 38 34 27 28 29 28 49 9 26 19 14 14 28 23 5

11 13 39 31 15 18 43 36 5

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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 practicePlease 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 IndustrialHealthcare 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 Connection Centrality

Cross Boundary Analysis

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Density Across Practice

Step 1. Network > Cohesion > DensityStep 2. Input dataset “infoge4.##h”Step 3. Row Partitioning “Attrib col 3Step 4. Column Partitioning “Attrib col 3

Tip: Col 3 is the column that includes the practice attribute. You can selectdifferent columns for different attributes

Tip: Col 3 is the column that includes the practice attribute. You can selectdifferent columns for different attributes

Network Connection Centrality

Cross Boundary Analysis

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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 > BrokerageStep 2. Input dataset “infoge4.##h”Step 3. Partition vector “attrib col 2”

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

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

Network Connection Centrality

Cross Boundary Analysis

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Additional Quantitative Analysis

Symmetrization & Verification

Scatter Plots

Combining Networks

QAP Correlation and Regression

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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)

Bill John

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Symmetrizing Data (Continued)

Step 1. Transform > SymmetrizeStep 2. Input dataset “infoge4.##h”

Step 3. Symmetrizing method “maximum”Step 4. Output dataset “Syminfoge4.##h”

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

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

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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

Verification of Asymmetric Data

Step 1. Tools > Matrix algebraStep 2. In the Enter Command box type “newinfo = average(transpose(infofrom),infoto)”Step 3. Enter

Tip: The new matrix “newinfo” cannow be used for various visual and quantitativeanalysis.

Tip: The new matrix “newinfo” cannow be used for various visual and quantitativeanalysis.

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Scatterplots

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

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Scatterplots (Continued)

Step 1. Tools > ScatterplotStep 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

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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 AlgebraStep 2. In the Enter Command box type “infovalue = mult(infoge4,valuege4)”

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

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

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QAP Correlation

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

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QAP Regression

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

Step 2. Dependent variable “InfoGE4”Step 3. Independent variable “ValueGE4”

Adjusted R-Square of 0.214 indicates a moderate relationship between the two social relations.

Theprobability of 0.000 indicates that it is statisticallysignificant.

Adjusted R-Square of 0.214 indicates a moderate relationship between the two social relations.

Theprobability of 0.000 indicates that it is statisticallysignificant.