Explore UCD

UCD Home >

STAT41010

Academic Year 2024/2025

Stat Network Analysis (STAT41010)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Alberto Caimo
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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.

About this Module

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
Lectures

24
Computer Aided Lab

12
Specified Learning Activities

24
Autonomous Student Learning

60
Total

120

Approaches to Teaching and Learning:
Weekly lectures and computer labs.

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
Incompatibles:
STAT40960 - Stat Network Analysis (online)


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Continuous assessment Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Other No
40
 No
Exam (Take-Home): Take-home exam or final project Week 8, Week 9, Week 10, Week 11, Week 12, Week 14, Week 15 Other No
60
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Summer 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.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Mon 11:00 - 11:50
Spring Computer Aided Lab Offering 1 Week(s) - 21, 23, 25, 29, 31, 33 Mon 12:00 - 12:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 12:00 - 12:50
Print this page
Fill in the following details:
Enter the body of your query here.

From time to time UCD would like to send you further information that we feel, based on your enquiry, would be of interest to you.