Planning a Social
Network Analysis
Digital Promise
Accelerating Innovation in Education
Planning a Social Network Analysis |
2
Table of Contents
Introduction .......................................... 3
Planning a Social Network Analysis ......................4
Step 1: Establish the network’s basis for your research ........... 4
Step 2: Develop and refine research questions ..................4
Step 3: Determine type of data to collect ...................... 5
Step 4: Select data collecting tools ............................ 6
Step 5: Select data collecting method/processes ...............6
Step 6: Analyze the data .....................................6
SNA Examples from EdClusters .........................10
Atlanta ....................................................10
Madison ..................................................10
Rhode Island .............................................. 11
Tucson ................................................... 11
Conclusion .......................................... 13
Appendix ............................................ 14
Drafting and Launching Surveys ........................ 15
Sample Survey Questions ................................... 15
Network Mapping Example ............................22
Additional Resources .................................29
Further Reading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Additional Survey Resources ................................29
Network Mapping .........................................29
Network Data Examples ....................................29
Planning a Social Network Analysis |
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Introduction
This toolkit provides a simplified approach to Social Network
Analysis (SNA), which is a research method of understanding
relationships and connections between individuals, groups,
and things. This approach helps us understand who is
working with whom, how information is given or acquired,
how power is concentrated or shared within an organization,
and how special interest groups form and function.
1 2 3
In the education sector, SNA combined with
questions about the qualities of the people
or organizations in the network can help
us understand pressing issues and uncover
opportunities in education in specific regions
such as: how teachers engage with community
partners, how communities can better
support education in their regions, or how to
increase students’ access to social capital.
The following pages are meant to help
practitioners understand more about the
basics of SNA, how to plan to conduct one,
ways to collect and analyze data, options for
mapping network data, and other resources.
While SNA requires a concerted eort and
an ability to find patterns and connections
from data, this toolkit will guide practitioners
so they can customize their approach
based on time and funding constraints.
The content for this toolkit came from a
variety of resources, but some of the major
sources include: Introduction to social
network methods, Social Network Analysis:
An Introduction, Social Network Analysis
(Wikipedia), and Diusion Levers Toolkit.
1 http://www.kstoolkit.org/Social+Network+Analysis
2 https://en.wikipedia.org/wiki/Social_network_analysis
3 http://www.orgnet.com/sna.html
Planning a Social Network Analysis |
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Planning a Social Network Analysis
The following sections will take you through the process of
planning an SNA. As you read through the following sections,
determine the tasks that you would need to complete for
your specific network analysis and think about a potential
timeline for your work.
Step 1: Establish the network’s basis for your research
Before starting an SNA, you will need to determine the type
of network you have.
Networks come in dierent shapes and sizes, and it is important to determine the kind
of membership your network comprises to determine who will be surveyed. Knowing
your network will help you understand what kind of information you can get from it.
A bounded network is a network with
a set number of network members
(e.g., students in a classroom).
An unbounded network is a network
that does not have set membership (e.g.,
weekly meetup group with an open
invitation to anyone in the community).
Step 2: Develop and refine research questions
Like other types of analysis, an SNA will be driven by your
research questions; they will provide guiding direction,
influence the data collection process, and shape your
methods for data analysis.
Most research questions that guide an SNA
analysis will be focused on descriptive
or exploratory research. This kind of
research will help you understand the
composition and function of your network.
You can also have research questions that
focus on understanding an intervention
in your network or evaluating the impact
of a network program or service.
Planning a Social Network Analysis |
5
Exploratory research could focus on
several things, including identifying:
• Central individuals/organizations
in your network
• Knowledge/information brokers
• Isolated members and bottlenecks
• Knowledge/information flow
• Informal networks
4
Research questions should define your subject/
network of interest, describe your topic of
investigation, and define the outcome you
plan to measure. Example questions include:
• What organizations are formally connected
to the Oz learning ecosystem? In what ways
do they contribute to the ecosystem?
• Who are the newest members of the
Gotham learning ecosystem? What
are the entry connections that help
individuals join the network?
4 https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/6381.pdf
5 http://faculty.ucr.edu/~hanneman/nettext/C1_Social_Network_Data.html
6 https://docs.kumu.io/guides/sna-network-mapping.html
7 https://docs.kumu.io/guides/sna-network-mapping.html
8 http://www.analytictech.com/networks/whatis.htm
Step 3: Determine type of data to collect
When collecting data on networks, it is also important to
determine the type of connection data you want to collect.
In order to conduct an SNA, you need to
collect relational data. This is data that
reveals some kind of connection between the
individuals, groups, or things in the network.
5 6
This data can come from surveys that
you collect from members in the network
you are analyzing. It could come from
existing data, like public datasets on
organizational connections, data on social
media connections, datasets from CRMs (like
Salesforce), etc. And it can come from your
own knowledge of the relationships that
exist in the network you are analyzing.
7
Here are some categories of relational data you
might consider collecting from respondents:
8
• Social roles (supervisor, teacher,
friend, acquaintance, etc.)
• Kinship (e.g., sister, brother, cousin, etc.)
• Aective (like, dislike, respect, etc.)
• Resource (knowledge, facility
access, resource access, etc.)
• Actions (talk with, meet with,
collaborate with, eat with, etc.)
• Distance (number of miles between, etc.)
• Co-occurrence (same organization,
same school, etc.)
The relationship data could be in the form of:
• Simple binary data like yes or no
(connected vs. not connected; like or dislike)
• Categorical data or categories/
ranks (e.g., like, dislike, like the
most, dislike the most, etc.)
• Interval data or simply numbers (e.g.,
number of times you communicated,
number of events you attended
together, number of projects you
have worked on together, etc.).
Planning a Social Network Analysis |
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Step 4: Select data collecting tools
The most common data collection methods used in SNA are
surveys and interviews.
A survey should include questions regarding
the background of the respondent and a
way for them to provide information on
connections. For a bounded network,
you should consider providing a list of all
members in the network (possible ways to
get this information includes lists of program
participation, attendance at events, etc). If
you plan to use a snowball sampling method
(see Step 5), your survey should include a
section for respondents to list connections.
There are many banks of survey questions
that have been used in SNA research made
publicly available, some of which we have
included at the bottom of this document.
You can recycle these questions in your own
surveys and adjust them to your needs.
Step 5: Select data collecting method/processes
When planning data collection for an SNA, you need to
determine the sample that you will draw from. Two popular
sampling methods include:
• Full Network Method: Collecting
data from every member of your
network (or network subset that you
are investigating). This method works
with a bounded network. You may not
be able to get everyone, but the more
people you get, the more complete your
understanding of the network will be.
• Snowball Method: Starting with a core
group of network members, you collect
data on all of their connections. Then you
reach out to the new connections and
collect data on all of their connections.
This continues until you cannot surface any
more new members or until you run out
of time. This method will miss members
who are not connected to the people
sampled and may bias your sample; on the
other hand, it may also help you access a
wider sample of network members than
you could have identified on your own.
Step 6: Analyze the data
Visual analysis, like mapping a social network, is usually used
when conducting an SNA. Using your relational data, you can
then begin to develop a network model.
Planning a Social Network Analysis |
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Networks are made up of nodes and paths.
Nodes are the actors—individuals, groups, or
things—that make up the network.
9
Paths are
the lines (or edges) that connect the nodes
together.
10
Paths can dier based on the kinds
of interactions happening between nodes.
One important characteristic of paths is
directionality. Some networks are undirected,
so a simple path (or line) exists between two
nodes. Other networks are directed, so paths
flow in a certain direction. In a directed graph,
the paths are represented as a line with an arrow
at one or both ends to indicate the direction
of a connection (e.g., you follow someone on
Twitter, but they don’t follow you).
11 12
edge
node
directed undirected
9 http://faculty.ucr.edu/~hanneman/nettext/C1_Social_Network_Data.html
10 https://www.linkedin.com/learning/social-network-analysis-using-r/what-you-should-know-before-watching-this-course
11 https://www.e-education.psu.edu/geog597i_02/node/832
12 http://faculty.ucr.edu/~hanneman/nettext/C7_Connection.html
13 Image from Medium article “Analysing data networks”: https://medium.com/graph-commons/analyzing-data-networks-
f4480a28fb4b
Through visually depicting a network, you can explore the connections and patterns that exist
and make conclusions based o of that exploration. For example, the following image illustrates
how you can visually break down a network.
13
Planning a Social Network Analysis |
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Once you have your data prepared, you
have a lot of options to consider. You can
use a mapping software or map your data
by hand; you can use free software or paid
software that might be easier to use and
provide more features; or you can create
static maps (just an image) or interactive
maps that you embed into a website.
Most network mapping software require a
“From” and a “To” column in your relational
dataset. When you import the data you
will need to specify if the map is directed
or undirected. You can also add variables
about the type of connection (see first table)
and additional qualitative data about the
people or organizations (second table). This
additional information will let you see more
about the patterns of your connections.
Mapping tools will also allow you to add additional datasets.
14 15 16
Label Bio
Harry Potter The boy who lived. Main character of the series.
Lord Voldemort The antagonist of the series who murdered many.
Hermione Granger One of Harry’s best friends. Marries Ron Weasley.
From To Type
Harry Potter Lord Voldemort Negative
Harry Potter Hermione Granger Positive
Hermione Granger Ron Weasley Positive
Hermione Granger Draco Malfoy Negative
14 http://faculty.ucr.edu/~hanneman/nettext/C6_Working_with_data.html
15 You can find additional network data examples here: https://snap.stanford.edu/data/
16 The Harry Potter data can be found here: http://dpmartin42.github.io/projects/Harry_Potter/Harry_Potter_Network.html
Here are popular options for mapping
your data:
• Kumu is a user-friendly tool that helps users
make attractive network graphs. It is free
for public projects, but users have to pay
a monthly fee for private use. They also
provide several step-by-step guides to help
you upload your data and start mapping
your network.
• Gephi and Cytoscape are free, open-source
platforms built specifically for network
modeling and analysis. They provide a broad
range of features for SNA.
• R is a completely free, open-source software
for analyzing data with robust network
mapping capabilities. To map your data in R
you have to do some coding/scripting. There
are a lot of forums and resources online to
get help with your R projects.
Planning a Social Network Analysis |
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Further, more tools are available in a curated
list of social network analysis visualization
tools put together by KDnuggets.
Through this analysis, there are several
ways to examine connections and to
analyze your network. Here are some
of the ways to look at connections:
• Connectedness/Centrality: Number of
connections one node has to other nodes
17
• Density: Number of connections divided
by total possible connections
18
• Betweenness: Measures if a node stands
between other nodes (bridging)
19
• Clique: A group of nodes where
all possible links are present
20
• Component: A group of connected nodes
21
• Closeness: How close a node is to
all other nodes (shorter path to other
nodes increases closeness)
22
• Degree: Number of connections
23
• Measures of power: Being
connected to connected nodes
24
• Homophily: How similar or dissimilar
network members are from their
connections (demographics,
education, occupation, etc.)
25
• Multiplexity: Number of connections
between two network members (e.g., you’ve
worked together on several projects).
26
• Reciprocity: The level to which
a connection is reciprocal
27
• Propinquity: Degree to which
individuals have more ties with people
geographically close to them
28
Quantitative analysis can also be used to
analyze network data. Your analyses should
be accompanied by some descriptive statistics
on your network (breakdown of members by
stakeholder group, by gender, by region, etc.).
You can also use more advanced statistical
models, which we are not going to cover
here, but some of the network mapping
applications can do these analyses for you.
You may also want to use qualitative
analysis to understand the patterns that
you are seeing in your network. This could
include interviewing members or observing
situations (like a convening or a design
session) that help you understand why
some of the patterns exist. If you have
time, you could also do additional surveys
and interviews to ask network members
more about the patterns you are finding.
17 https://www.lsu.edu/faculty/bratton/networks/closeness.ppt
18 http://www.the-vital-edge.com/what-is-network-density
19 https://en.wikipedia.org/wiki/Betweenness_centrality
20 https://www.safaribooksonline.com/library/view/social-network-analysis/9781449311377/ch04.html
21 https://en.wikipedia.org/wiki/Connected_component_(graph_theory)
22 https://www.sci.unich.it/~francesc/teaching/network/closeness.html
23 https://docs.kumu.io/guides/sna-network-mapping.html
24 https://www.lsu.edu/faculty/bratton/networks/closeness.ppt
25 http://aris.ss.uci.edu/~lin/52.pdf
26 https://en.wikipedia.org/wiki/Social_network_(sociolinguistics)
27 https://en.wikipedia.org/wiki/Reciprocity_(network_science)
28 https://en.wikipedia.org/wiki/Propinquity
Planning a Social Network Analysis |
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SNA Examples from EdClusters
Digital Promise worked with EdClusters on strategic, short-term,
or exploratory research for their regions over the course of four
months in 2018 to leverage a form of social network analysis
to better understand their Clusters’ networks. Atlanta, Madison,
Rhode Island, and Tucson shared their preliminary findings.
Atlanta
Community Guilds in Atlanta wanted to
understand the value that stakeholders
in the region were bringing to maker
education eorts. They received a
grant from a foundation to convene
all organizations in the local maker
education eort. At those convenings,
they discussed how the ecosystem around
maker education functions in Atlanta.
After these conversations, they used a value
mapping approach to show the major players
in the region and illustrate the value that each
group brings to the network. They found that
facilitated in-person meetings were a more
impactful form of data collection for them than
surveys, as it allowed their reach to expand
beyond the five organizations they originally
included in their bounded network sample size.
Madison
We Think Big is an organization in Madison
working to convene education stakeholders
and catalyze education innovation in
the region. As an emerging EdCluster,
they wanted to better understand how
the education organizations in Madison
were developing partnerships.
They conducted an SNA that consisted of
surveying education stakeholders across the
Madison education ecosystem and asking
respondents about their organizational
connections. Organizations filled out surveys
over a four-week period in the summer.
From their analysis, they learned the following
about their ecosystem: “The ‘profile’ of
what could be an ideal collaboration
partner was fairly consistent, with high
marks for partners who could influence
and bring partners together, add value to
the project, and who had alignment with
their own mission, objectives, and goals.”
This SNA has set the stage for the Madison
education ecosystem to have productive
conversations and build deeper collaborations that
will help drive innovative education in the region.
Planning a Social Network Analysis |
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Rhode Island
EduvateRI is a convener of the Rhode
Island EdCluster. They wanted to
understand how the education ecosystem
in Rhode Island has evolved over time and
understand how the current education
network in Rhode Island is connected.
To gather data for their SNA, EduvateRI
engaged in targeted outreach to ensure
many members of their education ecosystem
provided responses about the network.
They asked respondents to rate the
quality of their programming and indicate
their trusted professional connections.
In addition, they asked questions about
the ecacy of EduvateRI’s work.
After analyzing their data, EduvateRI
understood more about the stakeholders
who actively participate in the education
ecosystem. They found that current
educators and nonprofit leaders are the
most actively engaged, and that there
is less participation from government,
funder, and corporate stakeholders.
Active network members come from a
range of backgrounds, but one reason that
a majority of members are involved in the
network is for professional networking.
The findings from this SNA will help
EduvateRI to better track and improve
their programming going forward. This
includes better communication of the
power of the network to improve education
in Rhode Island and better leveraging the
expertise of members in the network.
Tucson
LeadLocal and CommunityShare are two
organizations working to build an innovative
and equitable education network in Tucson.
They engaged in an SNA because they wanted
to understand more about how teachers in
Tucson engage with community partners.
For their SNA, they designed and printed a
survey for teachers at three schools in the
Tucson region. There were 42 educators at
the schools who ended up taking the survey
about their community connections.
From their analysis, they learned that a
majority of teachers have no community
connections in the education ecosystem.
Learning resource professionals (e.g.,
librarians, counselors) had the highest number
of community connections. And elementary
school teachers were most likely to invite
community partners into the classroom.
Through the surveys they were also able to
learn about the specific kinds of community
partners that the teachers engaged with.
Planning a Social Network Analysis |
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This SNA spurred schools into thinking more
about their community connections, and one
school involved in the SNA is now going to
track community connections each quarter.
LeadLocal and CommunityShare plan to
use this SNA data to continue to build a
knowledge base and understanding of the
school-community connections in their
region and use that information to continually
strengthen their education ecosystem.
Planning a Social Network Analysis |
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Conclusion
SNA is a powerful tool for educational ecosystems seeking
to better understand the individuals and groups that
comprise them and the relationships that drive the work.
It is an adaptable research methodology that can help
identify deficits in and possibilities for collaboration that may
provide insights toward better understanding educational
ecosystems.
Planning a Social Network Analysis |
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Appendix
SNA is a powerful tool for educational ecosystems seeking
to better understand the individuals and groups that
comprise them and the relationships that drive the work.
It is an adaptable research methodology that can help
identify deficits in and possibilities for collaboration that may
provide insights toward better understanding educational
ecosystems.
Drafting and Launching Surveys ........................ 15
Sample Survey Questions ................................... 15
Network Mapping Example ............................22
Additional Resources .................................29
Further Reading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Additional Survey Resources ................................29
Network Mapping .........................................29
Network Data Examples ....................................29
Planning a Social Network Analysis |
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This section provides additional information and question
examples that can be used in drafting surveys.
Drafting and Launching Surveys
Sample Survey Questions
Questions may be used as is or modified.
Background Information
If you are developing questions from scratch or refining your questions, SurveyMonkey
developed a great survey writing guide to help people through the process.
If you are considering providing incentives for your survey (a quality network analysis usually
requires a high response rate), SurveyMonkey also developed a great guide on providing incentives.
There are three major sections you should consider including in your survey:
• Background information: Usually to create a network map you’ll need identifiable
information in your data. You need to know about the individuals/organizations
that are part of the network in order to map and analyze the network.
• Connection/relational information: Relational information can encompass a
variety of things including communication, collaboration, trust, expertise, roles,
distance, etc. It is important to keep this section focused and simple, so decide
early on what type of relational data is most important to you and your team.
• Additional qualitative data: You can also include additional qualitative data on your
survey (biography, role, satisfaction, etc.) and you can include that in your survey.
Name Your name:
Title Your title (if applicable):
Organization Your organization or school (if applicable):
Time at
Organization
Length of time at your organization or school (if applicable):
Time in Network When did you join the network (mm/yyyy):
29
29 https://drive.google.com/file/d/1gFBMwrqKXNg0rGKaOpHNuDovyeapvuO1/view?usp=sharing
Planning a Social Network Analysis |
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Stakeholder Group Which of the following groups do you primarily represent (please
select only one):
Education (Educator/School/District)
Government
Nonprofit
Funder
Researcher
Business/Entrepreneur
30
30 https://drive.google.com/file/d/1gFBMwrqKXNg0rGKaOpHNuDovyeapvuO1/view?usp=sharing
31 https://drive.google.com/file/d/1gFBMwrqKXNg0rGKaOpHNuDovyeapvuO1/view?usp=sharing
Collaboration
Example 1
For each person, check the box that best describes how often you
have worked together in the last six months (e.g., plan an event, write
a report, conduct an observation).
Not in last 6 months
Once in last 6 months
Multiple times in last 6 months
Multiple times a month
Weekly
Multiple times a week
Don’t know person
It’s me
31
Collaboration
Example 2
Check your connection to this person:
I know this person
I have talked/shared ideas with this person in the last 6 months
I have worked with this person in the past,
but not in the last 6 months
I have interacted regularly/collaborated with
this person in the past 6 months
Connection Information
Planning a Social Network Analysis |
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Collaboration
Example 3
Select how you and this person/organization collaborate (select all
that apply):
Developed an informal relationship
Bring together diverse stakeholders
Meet regularly
Exchange information/knowledge
Share resources
Engage in collective decision making
Share mission and goals
32
Communication
Example 1 (broad)
Who do you communicate with? (check all that apply)
Person 1
Person 2
Person 3
(Note, you can provide a list of all persons
or let people write in names)
Communication
Example 2
(specific)
Please check the box that best represents how often you commu-
nicated with each person in the last six months (e.g., in writing, over
the phone, face to face, or in meetings). If you don’t know the per-
son, check the box marked “Don’t know person.” [NOTE: This works
best as a matrix question. You need to answer this question about
every person]
Not at all
Less than monthly
Monthly
Weekly
Daily
Don’t know person
33
Learn Example 1
(broad)
Who do you go to to learn about [topic]? (check all that apply)
Person 1
Person 2
Person 3
(Note, you can provide a list of all persons
or let people write in names)
32 http://partnertool.net/tools-and-training/partner-tool/resources/#partner-template-materials
33 https://drive.google.com/file/d/1gFBMwrqKXNg0rGKaOpHNuDovyeapvuO1/view?usp=sharing
Planning a Social Network Analysis |
18
Learn Example 2
(specific)
For each person, check the box that best describes how often they
have provided you with information you used to do your work in the
last 6 months (e.g., new idea, a report, contact information, etc.).
[NOTE: This works best as a matrix question. You need to answer this
question about every person]
Not in last 6 months
Once in last 6 months
Multiple times in last 6 months
Multiple times a month
Weekly
Multiple times a week
Don’t know person
It’s me
34
Relationship
Example 1
[Put a checkbox for each question next to each person]
For each person, please answer the following questions.
Information Sharing: At least twice in the last month, have you re-
ceived information from this person that you need to do your job?
Problem Solving: At least twice in the last month, have you gone to
this person for help with work-related problems.
Support: At least twice in the last year have you gone to this person
for help with a dicult situation?
35
Relationship
Example 2
What kinds of activities does your relationship with this program/
partner/department entail:
None
Cooperative Activities: Involves exchanging information,
attending meetings together, and oering resources to partners
Coordinated Activities: Include cooperative activities
in addition to intentional eorts to enhance each
other’s capacity for the mutual benefit of programs
Integrated Activities: In addition to cooperative and
coordinated activities, this is the act of using commonalities
to create a unified center of knowledge and programming
that supports work in related content areas
36
34 https://www.surveymonkey.com/r/63R8XMT
35 http://partnertool.net/tools-and-training/partner-tool/resources/#partner-template-materials
36 http://www.durantlaw.info/sites/durantlaw.info/files/SNA_Survey.pdf
Planning a Social Network Analysis |
19
Snowball Sample
Question 1
Please identify up to 10 people who are important to you in your
professional network.
37
Snowball Sample
Question 2
List up to 10 people you feel are missing from our list of network
members.
Additional Qualitative Data
Expertise What is your expertise?
Authentic/Real-World Learning
Personalized Learning/Dierentiation
Teaching with Technology/Edtech Integration
Personalized Professional Development
Instructional Support/Leadership
Teacher Collaboration/PLCs
Teacher Recruitment and Retention
Student Learning: Literacy
Student Learning: Math/STEM
Student Learning: Kindergarten Readiness
Student Learning: College and Career Readiness
Mental Health and Trauma
Student/Teacher Relationships
Social-Emotional Learning
Formative Assessment
Assessment: Grading
Alternative Assessment Tools and Practices
Assessing Student Engagement
Assessing 21st Century Skills
Diversity
Opportunity Gaps
Culturally Responsive Practices
Summer Slide
Supporting Students Experiencing Poverty
Planning a Social Network Analysis |
20
Expertise
(continued)
Supporting English Learners
Family Engagement: Communication
Family Engagement: Increasing and Sustaining Engagement
Engaging Diverse Families
Connecting Family Engagement to Student Learning
Device /1:1
Data Interoperability
Open Educational Resources
Edtech Procurement/Adoption
Change Management
Public Relations/Stakeholder Engagement
Program Evaluation/Data-informed Decision Making
School Redesign
Student Pathways/Competency-based System
Interests What are you interested in?
Authentic/Real-World Learning
Personalized Learning/Dierentiation
Teaching with Technology/Edtech Integration
Personalized Professional Development
Instructional Support/Leadership
Teacher Collaboration/PLCs
Teacher Recruitment and Retention
Student Learning: Literacy
Student Learning: Math/STEM
Student Learning: Kindergarten Readiness
Student Learning: College and Career Readiness
Mental Health and Trauma
Student/Teacher Relationships
Social-Emotional Learning
Formative AssessmentAssessment: Grading
Alternative Assessment Tools and Practices
Assessing Student Engagement
Assessing 21st Century skills
Planning a Social Network Analysis |
21
Interests
(continued)
Diversity
Opportunity Gaps
Culturally Responsive Practices
Summer Slide
Supporting Students Experiencing Poverty
Supporting English Learners
Family Engagement: Communication
Family Engagement: Increasing and Sustaining Engagement
Engaging Diverse Families
Connecting Family Engagement to Student Learning
Device /1:1
Data Interoperability
Open Educational Resources
Edtech Procurement/Adoption
Change Management
Public Relations/Stakeholder Engagement
Program Evaluation/Data-informed Decision Making
School Redesign
Student Pathways/Competency-based System
Satisfaction Overall, how satisfied or dissatisfied are you with your experience
with [organization/program]?
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Satisfied
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Cytoscape is a free, open-source network mapping and
analysis platform. It is easy to get started in Cytoscape, and
this section provides some basics on how to quickly get up
and running.
Go to cytoscape.org and download the latest version of Cytoscape.
Network Mapping Example
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Open the Cytoscape application and drag and drop your network
connection data to the panel on the left side of the screen.
You will be prompted to select your “Source” and “Target” node
columns. You can also select the aspect of the network your additional
data is connected to (source node, target node, edge).
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Then you will get an initial map of your data. If you have additional
datasets that contain information on your nodes/edges, you can
drag and drop that data on the panel at the bottom of the screen.
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If you want to change the look of your map, you can select “Style”
on the left panel. You will then have options to format the nodes,
edges, and overall network. There is also a drop-down menu of pre-
set styles you can use.
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You can change the style of the network map based on your
descriptive data. Select the drop-down arrow next to the feature
you want to change and then select the column of data you want to
use to create your new style.
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Cytoscape also has built-in tools to help you analyze your
network. For example, you can go to Tools > NetworkAnalyzer >
Network Analysis > Generate Style from Statistics to change the
look of your map based on things like degree or betweenness.
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In the Apps menu, you can download additional apps to help you
analyze your network in ways beyond the standard analysis features
in Cytoscape.
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Further Reading
• Diusion Levers Toolkit
• Introduction to social network methods
• Social Network Analysis: An Introduction
• LinkedIn Learning SNA course
• Coursera SNA courses
Additional Survey Resources
• Social Network Survey Examples
• Example Network Survey on SurveyMonkey
• Survey Question Bank
Network Mapping
• Cytoscape.org
- Cytoscape user manual
- Cytoscape style directions
• Kumu.io
• Gephi.org
Network Data Examples
• Stanford Large Network Dataset Collection
• UC Irvine Network Data Repository
Additional Resources
