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Unit of study_

COMP4313: Large Scale Networks

Semester 1, 2023 [Normal evening] - Camperdown/Darlington, Sydney

The growing connected-ness of modern society translates into simplifying global communication and accelerating spread of news, information and epidemics. The focus of this unit is on the key concepts to address the challenges induced by the recent scale shift of complex networks. In particular, the course will present how scalable solutions exploiting graph theory, sociology and probability tackle the problems of communicating (routing, diffusing, aggregating) in dynamic and social networks.

Unit details and rules

Unit code COMP4313
Academic unit Computer Science
Credit points 6
Enrolment in a thesis unit. INFO4001 or INFO4911 or INFO4991 or INFO4992 or AMME4111 or BMET4111 or CHNG4811 or CIVL4022 or ELEC4712 or COMP4103 or SOFT4103 or DATA4103 or ISYS4103
Assumed knowledge

Algorithmic skills gained through units such as COMP2123 or COMP2823 or COMP3027 or COMP3927 or equivalent. Basic probability knowledge

Available to study abroad and exchange students


Teaching staff

Coordinator Lijun Chang,
Type Description Weight Due Length
Supervised exam
hurdle task
Final exam
Final exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Assignment Assignment 1
Problem Solving
15% Week 06
Due date: 31 Mar 2023 at 23:59
Outcomes assessed: LO2 LO3 LO6 LO7 LO8 LO9
Tutorial quiz Mid-term quiz
Answer multiple-choice questions online during Week 7's tutorial time
10% Week 07 50 Minutes
Outcomes assessed: LO1 LO9 LO7 LO6 LO5 LO3 LO2
Assignment Assignment 2
submit a pre-recorded presentation and a 4-6 pages report
25% Week 11
Due date: 12 May 2023 at 23:59
Outcomes assessed: LO1 LO3 LO4 LO9 LO10
hurdle task = hurdle task ?

Assessment summary

1. Assignment 1: Solving problems

2. Midterm Quiz: Answering multiple-choice questions online during Week 7’s tutorial time

3. Assignment 2: One of these three tasks:

a) Writing a short (4-6 pages) research paper exploring a research topic related to the course in LaTeX and presenting the related work and an analysis of this topic.

b) Programming an algorithm related to the course in C/C++, Java or Python and making a demo of it. Write a report on your findings (4-6 pages).

c) Analyses a real word graph dataset and identify interesting properties of the structure and the dynamics of the graph. Write a report on your findings (4-6 pages).

All three tasks includes submitting a short pre-recorded presentation on your findings and conclusions

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. A student must also achieve an overall final mark of 50 or more.

Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

For more information see

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late submission of the midterm quiz will get zero mark.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction (importance of today's networks, BFS, connected component...) Lecture (2 hr) LO4 LO5
Week 02 Graph ties Lecture (2 hr) LO1
Connected - how Kevin Bacon cured cancer by A. Talas Tutorial (1 hr) LO4 LO6
Week 03 Community Detection Lecture (2 hr) LO3
Manipulation of complex networks (NetworkX) Tutorial (1 hr) LO1
Week 04 Structural Balance and Network Evolution Lecture (2 hr) LO7
Twitter interaction Tutorial (1 hr) LO1 LO3 LO7
Week 05 Structure of web and hubs and authorities Lecture (2 hr) LO3 LO8
Network properties (betweenness, triadic closure...) Tutorial (1 hr) LO4 LO5
Week 06 Google's PageRank algorithm Lecture (2 hr) LO3 LO8
Computing the popularity of web pages Tutorial (1 hr) LO3 LO8
Week 07 Machine Learning on Graphs (I) Lecture (2 hr) LO4
Week 08 Machine Learning on Graphs (II) Lecture (2 hr) LO4
Feedback on Assignment 1 Tutorial (1 hr) LO2 LO3 LO6 LO7 LO8 LO9
Week 09 Information cascades and power laws Lecture (2 hr) LO2 LO6
Machine Learning on Graphs Tutorial (1 hr) LO4
Week 10 Structural Models for Small World Lecture (2 hr) LO9
Visualization of complex networks Tutorial (1 hr) LO4
Week 11 Peer to peer networks Lecture (2 hr) LO9 LO10
Power law distribution and decentralized search Tutorial (1 hr) LO2 LO6 LO9 LO10
Week 12 Project Presentation Presentation (3 hr) LO1 LO3 LO4 LO9 LO10
Week 13 Review Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed on the Library eReserve link available on Canvas.

D. Easly and J. Kleinberg, Networks, Crowds and Markets - Reasoning about a Highly Connected World. Cambridge University Press, 2010. 978-0-521-19533-1.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. interpret the fundamental structures, dynamics and resource distribution in such models
  • LO2. explain key factors that impact the accuracy and speed of information dissemination and aggregation
  • LO3. evaluate the asymptotic complexity and accuracy of graph algorithms
  • LO4. describe various types of network models in different contexts like computer science, society or markets
  • LO5. identify and assess accurately the role of networks in number of physical settings
  • LO6. identify and describe the technical issues that affect the dissemination of information in a network
  • LO7. analyse probabilistically the relations between communicating entities of a network
  • LO8. analyse the stochastic methods necessary to evaluate the convergence of various algorithms
  • LO9. recognise probabilistic solutions to problems that have no deterministic solutions and apply them thoroughly
  • LO10. compare experimentally and theoretically the adequacy of different probabilistic solutions.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities

This section outlines changes made to this unit following staff and student reviews.

Spend two weeks discussing graph machine learning in more detail.


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.