Introduction to Social Network Analysis

This course consists of two main sections. The first half of the course (approximately the first three days) describes and analyses social networks, or what is called 'network analysis'. The second half (approximately the last two days) builds on this by exploring how we can explain network structures or other aspects of sociopolitical life and investigate relational mechanisms using networks, or what is called 'network modelling'.

Key topics covered

Day 1: Networks and Relations

This session will introduce you to the theoretical assumptions and key terminology of network analysis. We will discuss what relations mean, how to collect network data, and the implications of design choices such as the boundary or type of network data to collect.

Day 2: Centrality and Cohesion

This session covers methods for measuring nodes centrality and embeddedness, as well as network measures such as how centralised the network is as a whole. We will discuss when to use different types of centrality and cohesion measures, and discuss the implications of network multimodality on these measures.

Day 3: Communities and Roles

This session investigates networks’ meso scale. We will explore the identification and emergence of groups or communities within networks, and identify and discuss the roles that nodes or ties may have in or between those groups. This session will also introduce blockmodeling.

Day 4: Topology and Diffusion

This session reviews several ideal typical network macro-structures or topologies and how they are created or generated. We then move to discussing models of diffusion and learning on networks, in particular the operation of threshold and compartment models upon networks that allow more complex models of diffusion to be explored.

Day 5: Formation and Change

This session will provide an overview of the bestiary of network models used to explain how networks are formed or change, including multiple regression quadratic assignment procedures, exponential random graph models, stochastic actor-oriented models, and relational event models such as the dynamic network actor model.

How the course will work online

The course is structured into five live Zoom sessions, each lasting 3 hours. The first 1.5-2 hours will focus on the major theoretical components of each day’s topic. The remaining 1 hour will be spent walking through practical tutorials, where you will apply the concepts and methods we discuss in the lecture. Additional exercises from the tutorials can be completed outside of class to consolidate your understanding of the theoretical and practical topics we have covered. The instructor will also conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Prerequisite Knowledge

Prior knowledge of R and statistical methods, including linear and logistic regression, is required for this class. In the practical exercises, you will receive hands-on instruction on conducting social network analysis using R. Installing the ‘migraph’ package from CRAN will install all required packages. While we will occasionally consider mathematical formulae, knowledge of linear or matrix algebra is not required.

Learning commitment

As a participant in this course, you will engage in a variety of learning activities designed to deepen your understanding and mastery of the subject matter. While the cornerstone of your learning experience will be the daily live teaching sessions, which total three hours each day across the five days of the course, your learning commitment extends beyond these sessions.

Upon payment and registration for the course, you will gain access to our Learning Management System (LMS) approximately two weeks before the course start date. Here, you will have access to course materials such as pre-course readings. The time commitment required to familiarise yourself with the content and complete any pre-course tasks is estimated to be approximately 20 hours per week leading up to the start date.

During the course week, you are expected to dedicate approximately one-three hours per day to prepare and work on assignments.

Each course offers the opportunity to be awarded three ECTS credits. Should you wish to earn a 4th credit, you will need to complete a post-course assignment, which will involve approximately 25 hours of work.

This comprehensive approach ensures that you not only attend the live sessions but also engage deeply with the course material, participate actively, and complete assessments to solidify your learning.

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.