Outline

Overview

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

Academic unit	Computer Science
Credit points	6
Prerequisites ?	DATA3888 or COMP3888 or COMP3988 or CSEC3888 or ISYS3888 or SOFT3888 or ENGG3112 or SCPU3001
Corequisites ?	None
Prohibitions ?	COMP5313
Assumed knowledge ?	A major in a computer science area. 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	Yes

Teaching staff

Coordinator	Lijun Chang, lijun.chang@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam Final exam	60%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Written work	Assignment 1 Problem Solving	10%	Week 06 Due date: 31 Mar 2026 at 23:59 Closing date: 05 Apr 2026	-	AI allowed
Outcomes assessed:
In-person written or creative task	Mid-term quiz Answer multiple-choice questions during Week 7's tutorial, specifically April 16.	10%	Week 07	40-50 minutes	AI prohibited
Outcomes assessed:
Written work	Group project Obtain a real-word graph dataset from online resources and write a program to identify interesting properties of the structure and dynamics of the graph. Document your findings in a report. Submit a presentation and a 4-6 pages report.	20%	Week 11 Due date: 15 May 2026 at 23:59 Closing date: 20 May 2026	-	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

1. Assignment 1: Solving problems

2. Midterm Quiz: Answer multiple-choice questions during Week 8's tutorial, specifically April 17, 20:00--21:00.

3. Assignment 2: Group project
Obtain a real-word graph dataset from online resources and write a program to identify interesting properties of the structure and dynamics of the graph. Document your findings in a report. Submit a pre-recorded presentation and a 4-6 pages report

Assessment criteria

The University awards common result grades, set out in the (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	Description
High distinction	85 - 100	听
Distinction	75 - 84	听
Credit	65 - 74	听
Pass	50 - 64	听
Fail	0 - 49	When you don鈥檛 meet the learning outcomes of the unit to a satisfactory standard. 听 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.听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 guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the听Academic Integrity Policy.

Visit the听Current Students website听for more information on AI in assessments, including听details on how to acknowledge its use.

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:

Extension of the midterm quiz is not allowed

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website听provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the听Academic Honesty Education Module (AHEM)听which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the听Current Students website听for more information on AI in assessments, including听details on how to acknowledge its use.

Learning support

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听simple extension.鈥� 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鈥檛 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听special consideration or special arrangements.

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

Using AI responsibly

Co-created with students,听听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.

Support for students

The Support for Students Policy听reflects the University鈥檚 commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

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

高清福利片 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,听Networks, Crowds and Markets - Reasoning about a Highly Connected World. Cambridge University Press, 2010. 978-0-521-19533-1.

Learning outcomes

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.

Outcomes
Graduate qualities

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鈥檚 personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

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

No major changes from last offering

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

高清福利片

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

高清福利片 commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

高清福利片

COMP4313: Large Scale Networks

Semester 1, 2026 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

高清福利片 commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links