COMP47270 Computational Network Analysis and Modelling

Academic Year 2021/2022

Many real-world systems can be represented as networks. One example which will motivate the analysis in this module is a social network where the nodes in the network represent individual people in society and links represent a social relationship such as friendship. The techniques we will study can be applied in many other contexts. In technology, interconnected computers can be represented as networks; in biology, interacting proteins can be represented as networks. A network analysis can reveal many interesting properties of these systems. Nowadays, many businesses are seeking to exploit the information in their customer databases in order to target services in a personalised way. There is a lot of interest in developing computational techiques to mine the information in the social networks contained in this data. This is challenging from a computational perspective, as the data-sets of interest tend to be very large. It is also challenging from a modelling perspective, as the structure of real-world networks is highly complex. This module will focus on modern computational techniques to extract information from large-scale networks. The module will have a practial focus, but will be grounded by network models developed in the last decade and a half.

NB. This is a professional module which is part of a professional MSc

Show/hide contentOpenClose All

Curricular information is subject to change

Learning Outcomes:

Students will have
- Learned how to characterise a network according to a number of measures.
- Compute network measures, with the assistance of appropriate computational tools.
- Carry out steps to analyse network data, from processing of raw data to extraction of network structure.
- Apply network analysis in an application such as recommendation, information diffusion etc.

Indicative Module Content:

Students will study the following topics:
- Measures of network characteristics, including clustering, degree distribution etc.
- Algorithms to find dense clusters in networks.
- Community finding in social networks.
- Information diffusion in networks.
- Trust networks and their application in recommender systems.

Student Effort Hours: 
Student Effort Type Hours
Lectures

24

Practical

36

Autonomous Student Learning

140

Total

200

Approaches to Teaching and Learning:
Teaching and learning approaches include: active/task-based learning; peer and group work; lectures; critical writing; lab work; enquiry & problem-based learning. 
Requirements, Exclusions and Recommendations
Learning Requirements:

Students taking this module should be experienced programmers. It is also a prerequisite to have a minimum of two years industrial software engineering experience. Please contact Dr. Mel Ó Cinnéide if you are eligible and wish to register for this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Programming/Data analysis assignments Coursework (End of Trimester) n/a Graded No

60

Class Test: In class examination. (Online in 2021) 2 hour End of Trimester Exam n/a Graded No

40


Carry forward of passed components
Yes
 
Remediation Type Remediation Timing
Repeat Within Two Trimesters
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.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 

There are no rows to display