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Social Network Analysis: From the Basics to Advanced Models in One Week - online


Course Information

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The concept of “social networks” is increasingly a part of social discussion, organizational strategy, and academic research. The rising interest in social networks has been coupled with a proliferation of widely available network data, but there has not been a concomitant increase in understanding how to analyse social network data.

This course presents concepts and methods applicable for the analysis of a wide range of social networks, such as those based on family ties, business collaboration, political alliances, and social media.

Course Code


Course Leader

Michael T. Heaney
Course Description

Classical statistical analysis is premised on the assumption that observations are sampled independently of one another. In the case of social networks, however, observations are not independent of one another, but are dependent on the structure of the social network. The dependence of observations on one another is a feature of the data, rather than a nuisance.

This course is an introduction to statistical models that attempt to understand this feature as both a cause and an effect of social processes. Since network data are generated in a different way than many other kinds of social data, the course begins by considering the research designs, sampling strategies, and data formats that are commonly associated with network analysis. A key aspect of performing network analysis is describing various elements of the network’s structure. To this end, the course covers the calculation of a variety of descriptive statistics on networks, such as density, centralization, centrality, connectedness, reciprocity, and transitivity. We consider various ways of visualizing networks, including multidimensional scaling and spring embedding. We learn methods of estimating regressions in which network ties are the dependent variable, including the quadratic assignment procedure and exponential random graph models (ERGMs). We consider extensions of ERGMs, including models for two-mode data and networks over time.

Instruction is split between lectures and hands-on computer exercises. Students may find it to their advantage to bring with them a social network data set that is relevant to their research interests, but doing so is not required. The instructor will provide data sets necessary for completing the course exercises.

The course covers:

  • Network theory
  • Research design for network data
  • Descriptive statistics for networks
  • Exponential random graph models (ERGM)
  • Extensions of ERGMs for two-mode and time-series data

By the end of the course participants will:

  • Develop network theories and hypotheses
  • Prepare network data for analysis in R
  • Estimate statistical models of social networks
  • Present the results of social network analysis


Prerequisite knowledge for the course includes the fundamentals of probability and statistics, especially hypothesis testing and regression analysis. This course assumes that students can interpret the results of Ordinary Least Squares, Probit, and Logit regressions.  They should also be familiar with the problems that are most common in regression, such as multicollinearity, heteroscedasticity, and endogeneity.  Finally, students should be comfortable working with computers and data. No prior knowledge of R or network analysis is required.

IMPORTANT: Please note that this course includes computer workshops. Before registering please check that you will be able to access the latest version of R Statistical software. Please bear in mind minimum system requirements to run software and administration restrictions imposed by your institution or employer with may block the installation of software.


The course will run online from 11 September 2023 to 15 September 2023 from 9am to 12pm (Noon) and from 1pm to 3pm during each of the five days of the course.


Welcome, course procedures, requirements, and objectives

Lecture 01: Introduction to social network analysis

Lecture 02: Major theories

Lecture 03: Research designs and data


Computer Exercises 01: Introduction to Network Analysis in R

Lecture 04: Descriptive statistics

Computer Exercises 02: Descriptive statistics

Lecture 05: Inferential network analysis


Lecture 06: Exponential Random Graph Models (ERGMs)

Computer Exercises 03: Exponential Random Graph Models (ERGMs)

Individual consultations


Lecture 07: Temporal Exponential Random Graph Models (TERGMs)

Computer Exercises 04: Temporal Exponential Random Graph Models

Individual consultations.  Participants should plan to work in the evening to refine their presentations for Friday morning. 

Friday 15 SEPTEMBER 2023

Student presentations

Lecture 08: Generalized Exponential Random Graph Models (GERGMs)

Computer Exercises 05: Generalized Exponential Random Graph Models (GERGMs)

Closing discussion

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11/09/202315/09/20230[Read More]

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