Introduction to Social Network Analysis

online course

Learn social network analysis for political research, mastering key concepts, methods, and practical applications using R.

Need to Know

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. You should expect to spend 1-3 hours outside of the core teaching hours consolidating the material covered in these classes.

In Depth

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.

Course leader

James Hollway is an Associate Professor at the The Graduate Institute, Geneva. His research develops multilevel and dynamic network theories, methods, and data for studying institutionalised cooperation and conflict on various social issues.

Target group

Social science researchers

Course aim

The abundance of network metaphors and new relational data signify how exciting the field of social network analysis is for political research. This course offers you an introduction to the fundamentals of social network analysis in a highly interactive online teaching environment. The main lecture programme covers central concepts in the network literature and discusses how these concepts are theoretically motivated, methodologically operationalised, and applied.

Through the course, you will learn: key network concepts and terminology; strategies for collecting, visualising, and analysing network data; a range of measures and models for answering theoretically-informed questions; and examples of their application to political science as well as examples for you to apply. Various in-class exercises encourage familiarity and reflection on these concepts. Then tutorials are designed to equip you with the skills and hands-on experience required to manage and analyse network data using R. This course is designed to bring a maximum of 16 participants through to an intermediate level of understanding, with an overview of more advanced options to support further exploration in the field.

By the end of this course, you will:
- understand fundamental social networks concepts;
- recognise a range of network mechanisms and structures;
- have a strong overview of social networks measures and models;
- know which measure or model to use to answer which (types of) question;
- apply and interpret the results of social network methods on social scientific datasets;
- be a discriminating consumer of network literature from across the social sciences.

It is important to note that this course serves as an introduction to these topics. While you will gain a solid understanding of the subject and practical experience, the course cannot cover advanced topics in depth. Overall, the course will equip you with the knowledge and skills to help you develop well thought through network research designs in political science.

Credits info

4 EC
You can earn up to four credits for attending this course.
3 ECTS credits – Attend 100% of live sessions and engage fully with class activities.
4 ECTS credits – Attend 100% of live sessions, engage fully with class activities and complete a post-class assignment.

Fee info

GBP 492: ECPR member - check whether your institution is a member here: https://ecpr.eu/Membership/CurrentMembers
GBP 985: ECPR non-member

Scholarships

Funding applications for the 2024 ECPR Methods School summer programme are now opening for applications. Apply before mid-April 2024. For more details on funding opportunities for ECPR's other events, please visit https://ecpr.eu/Funding/Funding