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

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. Selecting an Appropriate Group.

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

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

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

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

  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 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/

  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 give work-related information?” “From whom do you typically seek work-related information?” Network Measures Network Measures I do not typically seek information from this person  I do not typically give information to this Network Measures Network Measures I do typically seek information from this person  I do typically give information to this person

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

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

  34. Distance Cross Boundary Analysis Network Connection Centrality Long average distance Short 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 Cross Boundary Analysis Network Connection Centrality Average Distance is 3.5 Step 1. Network > Cohesion > Distance Step 2. Input dataset “infoge4.##h”

  36. Measures of Centrality Cross Boundary Analysis Network Connection 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. Degree Centrality Cross Boundary Analysis Network Connection 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 Cross Boundary Analysis Network Connection 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 Closeness of F is 13

  39. Betweenness Centrality Cross Boundary Analysis Network Connection 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 Betweenness of h is 28.33

  40. 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 Cross Boundary Analysis Network Connection Centrality Step 1. Network > Centrality > Degree

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

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

  46. 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. 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 Integrators High Info Sources # People Receives Information From High Info Seekers # People Each Person gives Information To

  48. 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

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

  50. Cross-boundary Analysis Cross Boundary Analysis Network Connection 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.

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