STAT40960 Stat Network Analysis (online)

Academic Year 2023/2024

Relational data describe interactions between entities, and are readily available in a variety of data science contexts. Examples include social networks (e.g. professional or friendship ties between individuals), political and economic networks (e.g. relations and trades between countries), and biological networks (protein-protein interaction networks).

The study of relational data is impactful in a variety of applied fields. For example, we can use social networks to study the spread of a disease within a community; we can use economic networks to study financial stability; and biological networks may help in the development of new treatments.

Relational data are big data that are characterised by complex dependency structures, which are difficult to unravel and to summarise. This has led to the introduction of a number of methods and algorithms that address these challenges. This module covers the theory and methodologies that can be employed to model and study relational data.

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Curricular information is subject to change

Learning Outcomes:

The student will familiarise with the different types of relational data and the related statistical models. The student will be able to manipulate data stored as a network, and to choose and implement appropriate statistical methodologies to analyse these data. The student will be able to summarise relational data using algorithms and models, by implementing them in the programming language R. The student will be able to interpret the results and draw conclusions from them.

Indicative Module Content:

Topological properties of networks. Erdos-Renyi random graph. Community detection. Stochastic Block Models. Latent Position Models. Exponential Random Graph Models. Network Autocorrelation Models.

The module has a strong focus on programming with R, and on the computational aspects of the statistical methodologies that are introduced.

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning


Online Learning




Approaches to Teaching and Learning:
Weekly video lectures and computer lab sheets. 
Requirements, Exclusions and Recommendations
Learning Requirements:

Background on statistical inference including probability spaces, likelihood-based inference, regression is essential. Students should be familiar with linear algebra and calculus.

Learning Recommendations:

Familiarity with R, or with a computer programming language that is related to data science.

Module Requisites and Incompatibles
STAT41010 - Stat Network Analysis

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Project: Final project Varies over the Trimester n/a Other No


Continuous Assessment: Continuous assessment Varies over the Trimester n/a Other No


Carry forward of passed components
Resit In Terminal Exam
Autumn No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
John O'Sullivan Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.

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