Study information

Network Science - 2025 entry

MODULE TITLENetwork Science CREDIT VALUE15
MODULE CODECOMM039 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS 12
Number of Students Taking Module (anticipated) 40
DESCRIPTION - summary of the module content

Many of the most important datasets are relational: friends and followers on social media, users who buy similar products, towns connected by roads, computers connected by routers and so on. Network Science is how we study and understand large relational data sets. In this module you will study how to represent, visualise, summarize and analyse large networks to learn about communities, epidemics, transport and resilience in real systems. This module is appropriate for students interested in data science and requires some programming and math background.

AIMS - intentions of the module

This module aims to give students the expertise to model and analyse large network datasets. After a grounding in the basics of network science we move on to more advanced techniques and algorithms to perform rigorous analysis of large networks and showing how these methods can be applied to real data sets.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge:

  1. Load, transform, summarise and describe large scale network data with appropriate statistical rigour

  2. Explain different applications of network science in real word scenarios

Discipline Specific Skills and Knowledge:

  1. Use and implement classic and modern network algorithms for analysing real world data with appropriate software packages

  2. Model real systems as networks, identifying the appropriate node entities and relational constraints e.g. directed, bipartite, planar etc.

Personal and Key Transferable/ Employment Skills and Knowledge:

  1. Selecting correct and rigorous methods of analysis from a large toolbox.

  2. See how one underlying mathematical model can apply to a wide range of systems and scenarios.

SYLLABUS PLAN - summary of the structure and academic content of the module

  • Basics of networks and graphs

  • Metrics and summary statistics

  • Path finding algorithms

  • Real world networks

  • Models (Erdos-Renyi, scale-free, configuration, stochastic block model)

  • Community Structure

  • Epidemics, SIR model

  • Spatial Networks

  • Dynamic Networks

  • Advanced network topics e.g. multiplex networks, hypergraphs, network embeddings

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 36 Guided Independent Study 114 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category  Hours of study time  Description 
Scheduled Learning and Teaching 24 Lectures
Scheduled Learning and Teaching 12 Workshops
Guided Independent Study 114 Reading, Workshop preparation

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of the assessment e.g. duration/length ILOs assessed Feedback method
Workshop 12 hours All In person, verbal discussion with the TA/Module lead

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of credit Size of the assessment e.g. duration/length ILOs assessed  Feedback method
Coursework - Presentation 30 15-minute presentation  All Written 
Coursework – Mini project 70 Approx 3000-word report All Written

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original form of assessment Form of re-assessment  ILOs re-assessed Time scale for re-assessment
Mini project Mini project + approx. 3000-word report All Referral/deferral
Presentation Mini project + approx. 3000-word report All Referral/deferral

 

RE-ASSESSMENT NOTES
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
 
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to submit a further assessment as necessary. If you are successful on referral, your overall module mark will be capped at 50%.
RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener
Basic reading:
  • Newman, M, 2018, Network, Oxford University Press
  • Barabasi, L, 2016, Network Science, Cambridge University Press
  • Menczer, Fortunato, Davis, 2020, A First Course in Network Science, Cambridge University Press

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Thursday 28th November 2024 LAST REVISION DATE Wednesday 21st May 2025
KEY WORDS SEARCH Network Science;

Please note that all modules are subject to change, please get in touch if you have any questions about this module.