COMP30850 Network Analysis

Academic Year 2023/2024

The objective of this module is to provide undergraduate students with a thorough introduction to graph and network analysis from a computer science perspective. The module will cover the basic concepts and key algorithms in network analysis, and discuss their use in the context of many real-world applications across a variety of domains. Students will learn to apply network analysis methods in practice through the medium of the Python programming language.

NOTE: Students taking this module must have previously completed the module COMP30760 "Data Science in Python".

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Curricular information is subject to change

Learning Outcomes:

On completion of this module, students will be able to:
1. Understand the core concepts and algorithms in network analysis.
2. Create appropriate network representations from real-world data.
3. Interpret, compare, and critically appraise different network representations.
4. Competently apply practical methods and tools for network analysis and visualisation.

Indicative Module Content:

The topics covered by this module may include:
- Basic concepts in graphs and networks
- Applications of network analysis
- Representing data as networks
- Network measures and metrics, including centrality
- Path problems and algorithms
- Network visualisation
- Dynamic networks
- Social media networks

Student Effort Hours: 
Student Effort Type Hours
Lectures

12
Practical

12
Autonomous Student Learning

70
Total

94
Approaches to Teaching and Learning:
Practical Labs; Continuous assessment 
Requirements, Exclusions and Recommendations
Learning Requirements:

Students must have previously successfully completed the module “COMP30760: Data Science in Python - DS”.


Module Requisites and Incompatibles
Incompatibles:
COMP30870 - Graph Algorithms


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Individual Project Assignment Unspecified n/a Alternative linear conversion grade scale 40% No

50
Class Test: Practical Test Unspecified n/a Alternative linear conversion grade scale 40% No

50

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

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Suchana Datta Tutor
Laura Dunne Tutor
Negin Zarbakhsh Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Spring
      
Exam Sem. 2 (ALU) Offering 1 Week(s) - 25 Thurs 09:00 - 09:50
Exam Spring (ALU) Offering 1 Week(s) - 28 Thurs 09:00 - 11:50
Laboratory Offering 1 Week(s) - 20, 21, 22, 23, 24, 25 Thurs 10:00 - 10:50
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25 Tues 11:00 - 11:50
Laboratory Offering 1 Week(s) - 20, 21, 22, 23, 24, 25 Tues 13:00 - 13:50
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25 Wed 14:00 - 14:50
Spring