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

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

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

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

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/

An Overview of UCINET

Transferring Data from Excel

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.

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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)

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.

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

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”

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

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”

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.

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

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

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

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

Pulling People Dynamically From the Network…

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

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.

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

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

# 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

# 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

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

Showing performance implications can quickly get people’s attention…

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.

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).

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

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

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”

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

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)

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”

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

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”

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

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.

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)”

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

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”