COMP0123 Complex Networks and Web

This database contains the 2018-19 versions of syllabuses.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Academic session



Complex Networks and Web



Module delivery

1819/A7P/T1/COMP0123 Postgraduate

Related deliveries

1819/A7U/T1/COMP0123 Masters (MEng)

Prior deliveries




FHEQ Level


FHEQ credits



Term 1

Module leader

Zhou, Shi


Zhou, Shi

Module administrator

Bottomley, Samantha


This module introduces essential concepts and methods in the interdisciplinary research area of network science, with a particular focus on the Internet, the World Wide Web and online social media networks. Topics covered include graphic properties and metrics of complex networks, mathematical models of networks, evolution of Internet topology, structures of the Web, network community detection, epidemic spreading models, analysis of social media networks, temporal networks, spatial networks, signed networks and network controllability.

Learning outcomes

On successful completion of the module, a student will be able to:

  1. Define and calculate essential network metrics.
  2. Describe the structure of the Internet and the Web.
  3. Relate graphic properties to network functions.
  4. Explore new angles to understand network collective behaviours.
  5. Design and conduct analysis on large networks.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MSc Web Science and Big Data Analytics
  • MRes Web Science and Big Data Analytics
  • MSc Financial Systems Engineering
  • MRes Financial Computing
  • MSc Software Systems Engineering
  • MSc Spatio-Temporal Analytics & Big Data Mining (and PGDip and Cert)
  • MEng Engineering (Electronic with Computer Science)


In order to be eligible to select this module, students must have a strong competency in programming.


  • Complex networks
  • Network graphic properties
  • Random networks
  • Small-world networks
  • Scale-free networks
  • Generative network models
  • Rich-club coefficient
  • Network mixing patterns
  • Network structural constraints
  • Network centrality
  • Internet topology and models
  • The Web structure
  • Network visualisation
  • Network community structure
  • Epidemic spreading models
  • Network controllability
  • Document networks
  • PageRank
  • Temporal networks
  • Spatial networks
  • Signed networks
  • Twitter botnets
  • Online social network analysis

An indicative reading list is available via


The module is delivered through a combination of lectures and a coursework where students will work on an individual project and produce a technical report.


This module delivery is assessed as below:



Weight (%)



Written examination (2hrs 30mins)




Individual technical report



In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.