Introduction to Social Network Analysis - Online Course
A 3-Day Livestream Seminar Taught by
John Skvoretz10:00am-2:00pm ET (New York time): Live lecture via Zoom
4:00pm-5:00pm ET: Live lab session via Zoom (Thursday and Friday only)
Our lives play out through the relationships we maintain with others. Much social research assumes that these relationships can be ignored, and focuses instead on how individual attributes influence such outcomes as success, health, and sense of well-being. Social network research takes a contrary view, placing explanatory power in the connections we have to others and how the overall patterning of those connections contributes to the important outcomes in our lives.
Starting November 11, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will consist of a 4-hour lecture that will include a hands-on exercise to be completed on your own after the lecture session is over. An additional lab session will be held Thursday and Friday afternoons, where you can review the exercise results with the instructor and ask any questions. Live attendance is recommended for the best experience. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
A social network perspective can provide novel explanatory variables (betweenness, centrality, structural holes, etc.) to account for why individuals and groups experience differential outcomes in a wide variety of settings. Here are some examples from the instructor’s current research projects:
- The differential adoption of evidence-based instructional practices as a function of networks of teaching and research discussion among STEM faculty.
- The diffusion of misinformation and competing narratives within and across online platforms.
- The extent of intergroup associations between school children in five European countries.
- Friendship and sexual contact networks among Latino men who have sex with men and their usage of medication to prevent HIV.
More specifically, the study of social networks focuses on relationships among the units of a population. It also investigates how the structure of these ties affects outcomes experienced by both the units and the population. Often the units are persons, but they may be families, households, corporations, or nation states. Social network analysis refers to the methods by which properties of social networks are described, quantified, and analyzed. This workshop is an introduction to these methods.
A social network perspective can provide novel explanatory variables (betweenness, centrality, structural holes, etc.) to account for why individuals and groups experience differential outcomes in a wide variety of settings. Here are some examples from the instructor’s current research projects:
- The differential adoption of evidence-based instructional practices as a function of networks of teaching and research discussion among STEM faculty.
- The diffusion of misinformation and competing narratives within and across online platforms.
- The extent of intergroup associations between school children in five European countries.
- Friendship and sexual contact networks among Latino men who have sex with men and their usage of medication to prevent HIV.
More specifically, the study of social networks focuses on relationships among the units of a population. It also investigates how the structure of these ties affects outcomes experienced by both the units and the population. Often the units are persons, but they may be families, households, corporations, or nation states. Social network analysis refers to the methods by which properties of social networks are described, quantified, and analyzed. This workshop is an introduction to these methods.
Computing
The course uses network analysis packages for the R environment (network, sna, statnet, igraph, Intergraph, ndtv) and used through the RStudio interface. Some familiarity with R and RStudio is helpful. Both the environment and the interface are free to download and use. Exercises that illustrate the concepts, measures, and types of analysis are plentiful and so students must have a device with the R environment and RStudio interface preinstalled. There are versions of these utilities for all major operating systems.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
The course uses network analysis packages for the R environment (network, sna, statnet, igraph, Intergraph, ndtv) and used through the RStudio interface. Some familiarity with R and RStudio is helpful. Both the environment and the interface are free to download and use. Exercises that illustrate the concepts, measures, and types of analysis are plentiful and so students must have a device with the R environment and RStudio interface preinstalled. There are versions of these utilities for all major operating systems.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
Who should register?
The study of social networks is an interdisciplinary field and so students from a variety of backgrounds are welcome: students from Sociology, Anthropology, Criminology, Political Science, Management, Public Health, Industrial Engineering, and Computer Science can all benefit from the workshop, although examples to illustrate concepts and for practice exercises are drawn primarily from social and political science.
The study of social networks is an interdisciplinary field and so students from a variety of backgrounds are welcome: students from Sociology, Anthropology, Criminology, Political Science, Management, Public Health, Industrial Engineering, and Computer Science can all benefit from the workshop, although examples to illustrate concepts and for practice exercises are drawn primarily from social and political science.
Suggested readings
The two paperback texts below are recommended. In addition, each session will have readings from the research literature made available as pdfs to participants.
Prell, C. 2012. Social Network Analysis: History, Theory, and Methodology. Los Angeles: Sage.
Robins, G. 2015. Doing Social Network Research: Network-based Research Design for Social Scientists. Thousand Oaks, CA: Sage.
The two paperback texts below are recommended. In addition, each session will have readings from the research literature made available as pdfs to participants.
Prell, C. 2012. Social Network Analysis: History, Theory, and Methodology. Los Angeles: Sage.
Robins, G. 2015. Doing Social Network Research: Network-based Research Design for Social Scientists. Thousand Oaks, CA: Sage.
Course outline
Day 1: Overview, data collection, package for analysis
Overview of social network analysis
• Motivating examples
• Basic vocabulary
• Research hypotheses investigated in the literature
Data collection
• Design considerations
• Sample instruments
Network analysis packages for the representation and visualization of
network data
• igraph
• sna
• intergraph
• ndtv
• sna
• statnet
Day 2: Network analytical variables
Node level variables
• Centrality
• Structural holes and brokerage
Group level variables
• Subgroups and community structures
• Blockmodels
• Positional structures
Day 3: Network and tie properties, statistical tests of network hypotheses
Ties, links, and edges
• Density and connectivity
• Strong and weak ties
• Weighted ties
• Small worlds and preferential attachment networks
Network hypotheses and methods for evaluating them
• Regression based methods
• Tests for dyad and triad patterns
• Exponential random graph models
Day 1: Overview, data collection, package for analysis
Overview of social network analysis
• Motivating examples
• Basic vocabulary
• Research hypotheses investigated in the literature
Data collection
• Design considerations
• Sample instruments
Network analysis packages for the representation and visualization of
network data
• igraph
• sna
• intergraph
• ndtv
• sna
• statnet
Day 2: Network analytical variables
Node level variables
• Centrality
• Structural holes and brokerage
Group level variables
• Subgroups and community structures
• Blockmodels
• Positional structures
Day 3: Network and tie properties, statistical tests of network hypotheses
Ties, links, and edges
• Density and connectivity
• Strong and weak ties
• Weighted ties
• Small worlds and preferential attachment networks
Network hypotheses and methods for evaluating them
• Regression based methods
• Tests for dyad and triad patterns
• Exponential random graph models
Payment information
The fee of $895 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $895 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.