Introduction to Social Network Analysis

A 3-Day Remote Seminar Taught by John Skvoretz, Ph.D.

Read reviews from other seminars taught by John Skvoretz

This seminar is currently sold out. To be added to the wait list, email

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 September 24, we are offering this seminar as a 3-day synchronous*, remote workshop for the first time. Each day will consist of a 4-hour live lecture held via the free video-conferencing software Zoom. Participants are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if they are unable to attend at the scheduled time.

Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on your own. An additional session will be held Thursday and Friday afternoons as an “office hour”, where participants can review the exercise results with the instructor and ask any questions.

*We understand that scheduling is difficult during this unpredictable time. 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, meaning that you will get all of the class content and discussions even if you cannot participate synchronously. 


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.


This remote seminar is held via Zoom, a free video conferencing application. Instructions for joining a session via Zoom are available here. Before the seminar begins, participants will receive an email with the meeting code and password you must use to join.  

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 on-line 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.

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.


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

RECENT COMMENTS FROM John Skvoretz’s Other Courses

“I thought this was a fantastic course. This course helped to clarify and expand upon what I had previously learned. Learning how to test if certain network structures are significantly different from chance was useful and getting a detailed explanation of how to interpret ERGM terms and ERGM model fit statistics was much better through this course than what I’ve previously tried to teach myself with textbooks.”
  Megan Evans, Pennsylvania State University

“The material is very relevant to audiences outside of its origins in sociology and related fields. It demonstrates the presence of mathematical rigor in an aspect of studying relationships that allows researchers to go beyond descriptive statistics and pretty pictures.”
  Steve Vejcik, TransUnion

“Very helpful for understanding advanced SNA. Nice instructor.”
  Huiwen Xu, University of Rochester

“This course was really helpful because I had access to real R scripts that I can use and replicate for my own research. John understands every detail of complex network models and explains them in a very clear way.”
  Seungho (Andy) Back, University of Toronto