Course Title: Complex Networks

Part A: Course Overview

Course Title: Complex Networks

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


171H School of Science


Sem 1 2017,
Sem 2 2019,
Sem 2 2023

Course Coordinator: Dr Stephen Davis

Course Coordinator Phone: +61 3 9925 2278

Course Coordinator Email:

Course Coordinator Location: 008.09.008

Pre-requisite Courses and Assumed Knowledge and Capabilities

Students will be asked to implement algorithms and analyse real network data, and hence should be confident and comfortable with at least one programming language. The course includes examples where algorithms are implemented in the programming environment R ( and this is the preferred option for students for carrying out assignment work.

Course Description

The world around us is brimming with structure that consists of discrete entities and relationships between those entities. These structures can be represented as a set of vertices and a set of links that formally define a graph. A complex network is nothing more than a very large graph where the links are neither predictable nor completely random. This course will present the mathematical and statistical techniques used to classify and characterise networks and then require you to work with real data sets to visualise and study networks that arise in ecology and epidemiology.

Objectives/Learning Outcomes/Capability Development


This course contributes to the following Program Learning Outcomes for BP083 Bachelor of Science (Mathematics) , BP245 Bachelor of Science (Statistics) and BH119 Bachelor of Analytics (Honours):

Knowledge and technical competence:

  • use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.


  • synthesise and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • balance the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

Upon successful completion of this course you should be able to:

  1. Identify and apply key concepts of basic graph theory as the mathematical basis for network science;
  2. Select and apply the mathematical and statistical techniques used to characterise network topology, node importance, and generation of null models in the form of random graphs, and implement all of these approaches in a programming environment;
  3. Recognise the common topological properties of complex networks that occur in a range of fields;
  4. Analyse real data sets to visualize and explore network data to gain insight into the structure and function of complex networks.

Overview of Learning Activities

Key concepts and their application will be explained and illustrated (with a variety of examples) in lectures and in online notes. Supervised problem-based lab classes will build your capacity to implement algorithms and work with real network data. You will learn to think critically and analytically and to obtain insight into the common structure found in complex networks that arise across a range of fields including the social sciences, biology, ecology and communication. Lectures will include in-class exercises to be solved in groups of 2 or more.  With  the help of the lecturer you will consolidate your knowledge of the key concepts and mathematical techniques used to analyse networks.

Overview of Learning Resources

You will be able to access course information and learning materials through Blackboard, which is part of myRMIT Studies. This will give you access to important announcements, lecture recordings (all lectures will be recorded), a discussion forum, staff contact details, the teaching schedule, online notes, a selection of relevant journal articles, assessment timelines and practice exam papers.

Some basic mathematics resources will also be available to you at for students wishing to solidify their knowledge of more fundamental mathematics. 

Overview of Assessment

Assessment Tasks:

Your ability to recognise and work with the mathematical and statistical methods described in class to measure and characterise complex networks will be assessed by:

Assessment Task 1: Programming oriented assignments
Four programming oriented assignments which you may complete using either Matlab or R as programming environments
Weighting 40%
This assessment task supports CLO2 and CLO4

Assessment Task 2: Problem Sheets 
Weighting 20%
This assessment task supports CLO1 and CLO2

Assessment 3: In-class Invigilated Assessment   
Two 90-minute ritten assessments will take place during the weekly 2-hour practical session. 
Weighting 40% 
This assessment supports CLO1, CLO2 and CLO3