Introduction to Social Network Analysis - Online Course
A 3-Day Livestream Seminar Taught by
Filip Agneessens10:00am-12:30pm ET (convert to your local time)
1:30pm-3:30pm ET
Interest in social networks has been climbing exponentially since the 1970s. For social scientists, networks can be seen as a fundamental adaptation that facilitates the coordination and distribution of resources, while simultaneously maintaining the flexibility of independent agents. For physical scientists, networks can be seen as universal structures underlying everything from molecules to galaxies. And for mathematicians and computer scientists, networks provide an abstract and propitious way of representing problems.
The field of social network analysis consists of a vocabulary of theoretical and mathematical concepts for investigating network phenomena, along with a distinctive data model and set of methodologies for collecting and analyzing network data. This course provides an overview of the core concepts and methods in social network analysis.
Starting December 14, we are offering this seminar as a 3-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
More details about the course content
At a theoretical level, we discuss such ideas as Granovetter’s “strength of weak ties” and “small worlds”. We also discuss core concepts, such as structural holes, homophily, and transitivity, as well as popular measures like closeness centrality, betweenness centrality, centralization, fragmentation, compactness, and structural equivalence. We will evaluate when specific measures might or might not be useful to answer a particular research question.
The course is very hands-on, emphasizing mastering the software and using the concepts and methods to answer research questions. To conduct social network analysis, we will make use of functions available in R. The core reading will be Analyzing Social Networks with R by Borgatti, Everett, Johnson, and Agneessens (2022).
We start this course by providing an overview of basic concepts, distinguishing between different types of networks, and then showing ways to visualize social networks with R. We also focus on measures of centrality, discuss the different types of research questions they can help answer, and how they can be calculated with R.
On the second day, we focus on some major theories, including Heider’s balance theory, Granovetter’s strength of weak ties, small worlds, and structural holes. We then go on to explore ways to measure structural holes and provide an overview of ego-network measures focusing on nodal attributes. Next, we turn to network-level measures, including measures of cohesion, centralization, reciprocity, and transitivity.
On the third day, we explore different ways to identify subgroups and communities. We also touch on ways to deal with two-mode networks. Finally, we introduce the concept of equivalence and consider specific situations where different versions of equivalence might be relevant/useful. We also touch on core-periphery and blockmodeling more generally.
At a theoretical level, we discuss such ideas as Granovetter’s “strength of weak ties” and “small worlds”. We also discuss core concepts, such as structural holes, homophily, and transitivity, as well as popular measures like closeness centrality, betweenness centrality, centralization, fragmentation, compactness, and structural equivalence. We will evaluate when specific measures might or might not be useful to answer a particular research question.
The course is very hands-on, emphasizing mastering the software and using the concepts and methods to answer research questions. To conduct social network analysis, we will make use of functions available in R. The core reading will be Analyzing Social Networks with R by Borgatti, Everett, Johnson, and Agneessens (2022).
We start this course by providing an overview of basic concepts, distinguishing between different types of networks, and then showing ways to visualize social networks with R. We also focus on measures of centrality, discuss the different types of research questions they can help answer, and how they can be calculated with R.
On the second day, we focus on some major theories, including Heider’s balance theory, Granovetter’s strength of weak ties, small worlds, and structural holes. We then go on to explore ways to measure structural holes and provide an overview of ego-network measures focusing on nodal attributes. Next, we turn to network-level measures, including measures of cohesion, centralization, reciprocity, and transitivity.
On the third day, we explore different ways to identify subgroups and communities. We also touch on ways to deal with two-mode networks. Finally, we introduce the concept of equivalence and consider specific situations where different versions of equivalence might be relevant/useful. We also touch on core-periphery and blockmodeling more generally.
Computing
This seminar will use R for the empirical examples and exercises. To participate in the hands-on exercises, you are strongly encouraged to have a computer with R and RStudio installed. RStudio is a freely available interface for R.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
This seminar will use R for the empirical examples and exercises. To participate in the hands-on exercises, you are strongly encouraged to have a computer with R and RStudio installed. RStudio is a freely available interface for R.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
Who should register?
This course is helpful for anyone who:
-
- Is looking for an introduction into social network analysis.
- Wants to get an overview of the main concepts and when they are useful.
- Wants to know how to obtain results using R.
Basic knowledge of R and RStudio can be beneficial. For those not familiar with R, a brief introduction will be available prior to the start of the first lecture.
For more advanced topics, check out Social Networks: Statistical Approaches.
This course is helpful for anyone who:
-
- Is looking for an introduction into social network analysis.
- Wants to get an overview of the main concepts and when they are useful.
- Wants to know how to obtain results using R.
Basic knowledge of R and RStudio can be beneficial. For those not familiar with R, a brief introduction will be available prior to the start of the first lecture.
For more advanced topics, check out Social Networks: Statistical Approaches.
Seminar outline
Day 1
Network visualization and basic network measures
-
- Introduction
- Different types of social networks (directed versus undirected, valued versus binary, one-mode versus two-mode, positive and negative ties)
- Types of network ties and how they are collected
- Basic ways to visualize networks with R
- Basic measures: density and degree centrality
Centrality measures
-
- Eigenvector and beta-centrality
- Closeness centrality
- Reach centrality
- Betweenness centrality
Day 2
Local measures of position
-
- Granovetter’s strength of weak ties
- Structural holes
- Measures of the resourcefulness of ego’s connections
- Different measures of homophily
- Measures of diversity/heterogeneity
Group/network-level measures
-
- Components
- Fragmentation
- Compactness
- Centralization
- Reciprocity
- Transitivity
Day 3
Subgroups, community detection and two-mode networks
-
- Cliques and n-cliques
- k-clans
- Girvan–Newman algorithm for community detection
- Louvain method for community detection
- Analyzing two-mode networks, one-mode projections and bipartite networks
Equivalence
-
- Structural equivalence
- Regular equivalence
- Core-periphery
- Blockmodeling
Day 1
Network visualization and basic network measures
-
- Introduction
- Different types of social networks (directed versus undirected, valued versus binary, one-mode versus two-mode, positive and negative ties)
- Types of network ties and how they are collected
- Basic ways to visualize networks with R
- Basic measures: density and degree centrality
Centrality measures
-
- Eigenvector and beta-centrality
- Closeness centrality
- Reach centrality
- Betweenness centrality
Day 2
Local measures of position
-
- Granovetter’s strength of weak ties
- Structural holes
- Measures of the resourcefulness of ego’s connections
- Different measures of homophily
- Measures of diversity/heterogeneity
Group/network-level measures
-
- Components
- Fragmentation
- Compactness
- Centralization
- Reciprocity
- Transitivity
Day 3
Subgroups, community detection and two-mode networks
-
- Cliques and n-cliques
- k-clans
- Girvan–Newman algorithm for community detection
- Louvain method for community detection
- Analyzing two-mode networks, one-mode projections and bipartite networks
Equivalence
-
- Structural equivalence
- Regular equivalence
- Core-periphery
- Blockmodeling
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.