CSYS5020: Semester 1, 2026

Units CSYS5020 Semester 1 2026 [Normal evening]

Unit outline_

CSYS5020: Resilient Networks and Systems

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

Overview

Our modern day technical infrastructure includes computer networks, transport networks, telecommunications, power systems, financial infrastructure and emergency services, all of which are growing more and more interconnected. Moreover, the behaviour of the modern infrastructure is not dependent only upon the behaviour of its parts: Internet of Things, complex electrical systems (such as modern power grids), communication and transport systems, megaprojects, social and eco-systems, generate rich interactions among the individual components with interdependencies across systems. This interdependent behaviour brings about significant new challenges associated with the design and management of complex systems. Cascading power failures, internet service disruptions, traffic disruptions, epidemic outbreaks, chronic diseases, financial market crashes, and ecosystem collapses are typical manifestations of these challenges, affecting the stability of modern society and technical infrastructure. This unit will develop an understanding of how interdependent systems perform under stress, how to improve resilience and how best to mitigate the effects of various kinds of component failure or human error, by more accurate analysis of interdependent cascades of failures across system boundaries. The studied topics will include dynamical analysis of complex interdependent networks, local and global measures of network structure and evolution, cascading failures, as well as predictive measures of catastrophic failure in complex adaptive systems, and the tools that enable planning for resilient infrastructure. This unit will equip future professionals with sufficient expertise and technical know-how for the design of efficient prevention and intervention policies, and robust crisis forecasting and management.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Mahendra Piraveenan, mahendrarajah.piraveenan@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
Practical skill	Assignment 1 Computing assignment	30%	Week 06 Due date: 03 Apr 2026 at 23:00 Closing date: 03 Apr 2026	-	AI allowed
Outcomes assessed:
Practical skill	Assignment 2 Calculation assignment	30%	Week 09 Due date: 01 May 2026 at 23:00 Closing date: 01 May 2026	-	AI allowed
Outcomes assessed:
Practical skill	Assignment 3 Calculation and computation assignment	40%	Week 13 Due date: 29 May 2026 at 23:00 Closing date: 29 May 2026	-	AI allowed
Outcomes assessed:

Assessment summary

The assignments will require you to integrate information from lectures and practicals to create a concise written argument

Detailed information for each assessment can be found on Canvas

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	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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 Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including 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.

Academic integrity

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

Our website 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 Academic Honesty Education Module (AHEM) 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 Current Students website for more information on AI in assessments, including 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 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.

Support for students

The Support for Students Policy reflects the University’s 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 to Resilient Networks and Systems	Workshop (2 hr)
Week 02	The world of networks	Workshop (3 hr)
Week 02	Topological analysis of complex networks: part 1	Workshop (3 hr)
Week 04	Topological analysis of complex networks: part 2	Workshop (3 hr)
Week 05	Cascading failures	Workshop (3 hr)
Week 06	Measuring network robustness and resilience	Workshop (3 hr)
Week 07	Spectral methods	Workshop (3 hr)
Week 08	Percolation in networks	Workshop (3 hr)
Week 09	Game theory in networked systems	Workshop (3 hr)
Week 10	Modelling epidemic spread in communities	Workshop (3 hr)
Week 11	Modelling networked financial systems	Workshop (3 hr)
Week 12	Modelling socio-ecological systems	Workshop (3 hr)
Week 13	Modelling power and transport systems	Workshop (3 hr)

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 through the Library eReserve, available on Canvas.

S. N. Dorogovtsev (Author), J.F.F. Mendes (Author), Evolution of Networks: From Biological Nets to the Internet and WWW (Physics) . Oxford University Press, 2003. 978-0198515906.

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. use elementary and intermediate programming skills to analyse, understand and simulate the dynamics of complex systems
LO2. apply topological analysis to a particular complex system to critically understand its structure
LO3. efficiently use existing software tools (e.g., Cytoscape, Pajek ) in complex network analysis and evaluating topological resilience
LO4. develop scientific programming skills which can be applied in network analysis
LO5. develop understanding of the nature and dynamics of interdependent systems
LO6. understand and apply elementary game theory in networked systems to simulate their dynamics and cognitive decision making of the participating entities
LO7. understand, and successfully use in analysis, the concepts of percolation, cascading failures, robustness, resilience, and related concepts within the context of interdependent systems
LO8. design basic network structures that satisfy structural and functional criteria within given domains and contexts, and are resilient to random failures, and various types of targeted attacks and disturbances.

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
Learning outcomes

Responding to student feedback

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

No significant changes have been made since this unit was last offered

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 online glossary.

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

Study 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