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Member rate £492.50
Non-Member rate £985.00
Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked
*If you attended our Methods School in the last calendar year, you qualify for £45 off your course fee.
Date: Monday 22 – Friday 26 July 2024
Time: 09:00 – 12:00 CEST
james.hollway@graduateinstitute.ch
Graduate Institute of International and Development Studies
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:
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.
3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment.
James Hollway is Co-Director of the Global Governance Centre, Head of the Environment and Sustainability Specialisation, and Associate Professor of International Relations/Political Science at the Graduate Institute of International and Development Studies in Geneva. His research develops multilevel and dynamic network theories, methods, and data for studying institutionalised cooperation and conflict on trade, health, and environmental issues such as fisheries and freshwater. His work has been published with Cambridge University Press, Journal of Conflict Resolution, International Studies Quarterly, Global Environmental Politics, International Environmental Agreements, Policy Studies Journal, Sociological Theory, Sociological Methodology, Social Networks, and Network Science.
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'.
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.
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.
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.
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.
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.
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.
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.
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.
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.