Outline

Overview

In our interconnected world, networks are an increasingly important representation of datasets and systems. This unit will investigate how this network approach to problems can be pursued through the combination of mathematical models and datasets. You will learn different mathematical models of networks and understand how these models explain non-intuitive phenomena, such as the small world phenomenon (short paths between nodes despite clustering), the friendship paradox (our friends typically have more friends than we have), and the sudden appearance of epidemic-like processes spreading through networks. You will learn computational techniques needed to infer information about the mathematical models from data and, finally, you will learn how to combine mathematical models, computational techniques, and real-world data to draw conclusions about problems. More generally, network data is a paradigm for high-dimensional interdependent data, the typical problem in data science. By doing this unit you will develop computational and mathematical skills of wide applicability in studies of networks, data science, complex systems, and statistical physics.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Linear algebra (matrices, eigenvalues, etc.); introductory concepts in statistics (statistical models, inference); a programming language
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Eduardo Goldani Altmann, eduardo.altmann@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Final Exam Final Exam covering the conceptual and mathematical aspects of the course.	33.34%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Written work	Project Report Written report on project, see EdStem for more details	16.67%	Week 13 Due date: 05 Nov 2025 at 23:59 Closing date: 05 Nov 2025	3000 words	AI allowed
Outcomes assessed:
Presentation	Project presentation Group Oral presentation	16.66%	Week 13 Due date: 06 Nov 2025 at 23:59 Closing date: 06 Nov 2025	20 minutes	AI allowed
Outcomes assessed:
Written work	Tutorial and lab assignments Coding and problem-solving.	33.33%	Weekly	1000 words each	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Tutorial and Lab Assignments (1/3 of final weight). Will require you to integrate the content of lectures into practical questions considered during the tutorials and computer laboratories. The focus will be on coding and practical implementations. There will be assignments in most weeks, with announcements and submissions taking place via EdSTem. Students may be asked to orally explain the content of their submission in class as part of the examination.
Final project (1/3 of final weight). The final project will include an oral presentation (in person during classes in week 13) and a written final report. Both parts have equal weights. The project will require the application and critical discussion of how the theory and computation techniques learned in class apply to new datasets and problem.
Final Exam (1/3 of final weight). There is one supervised final exam during the examination period at the end of Semester. The exam will cover all material in the unit from both lectures, tutorial, and laboratory, with focus on the theory. Hurdle: a mark of 40 (out of 100) in the final exam is required to pass this unit.

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	demonstrate the learning outcomes for the unit at an exceptional standard, including both mathematical and computational aspects of network theory
Distinction	75-84	demonstrate the learning outcomes for the unit at a very high standard, including both mathematical and computational aspects of network theory
Credit	65-74	demonstrate the learning outcomes for the unit at a good standard, including good mathematical understanding and basic computational skills related to network theory
Pass	50-64	demonstrate the learning outcomes for the unit at an acceptable standard, including the fundamental mathematic ideas of network theory and basic computational skills
Fail	0-49	don’t meet the learning outcome 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.

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:

- Assignments: Penalties apply to late submissions up to 7 days past the due date. In order to ensure timely feedback and alignment with the content of the lectures, a mark of zero will be awarded for all submissions more than 7 days past the original due date. Further extensions past this time will not be permitted. A mark adjustment using the final exam mark will be applied if needed. - Final project: no credit awarded for late submissions. - Exam: if a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

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	Lecture (3 hr)
Week 01	Week 1 Computer Laboratory and Tutorial	Computer laboratory (1 hr)
Week 02	Centrality measures	Lecture (3 hr)
Week 02	Week 2 Computer Laboratory and Tutorial	Computer laboratory (1 hr)
Week 03	Random Graph Models	Lecture (3 hr)
Week 03	Week 3 Computer Laboratory and Tutorial	Computer laboratory (1 hr)
Week 04	Random graphs vs. complex networks	Lecture (3 hr)
Week 04	Week 4 Computer Laboratory and Tutorial	Computer laboratory (1 hr)
Week 05	Mechanistic network models	Lecture (3 hr)
Week 05	Week 5 Computer Laboratory and Tutorial	Computer laboratory (1 hr)
Week 06	Inference: Importance sampling	Lecture (3 hr)
Week 06	Week 6 Computer Laboratory and Tutorial	Computer laboratory (1 hr)
Week 07	Community detection in networks	Lecture (3 hr)
Week 07	Week 7 Computer Laboratory and Tutorial	Computer laboratory (1 hr)
Week 08	Methods for high-dimensional inference	Lecture (3 hr)
Week 08	Week 8 Computer Laboratory and Tutorial	Computer laboratory (1 hr)
Week 09	Inference of large scale structures in networks	Lecture (3 hr)
Week 09	Week 9 Computer Laboratory and Tutorial	Computer laboratory (1 hr)
Week 10	Network Resilience	Lecture (3 hr)
Week 10	Week 10 Computer Laboratory and Tutorial	Computer laboratory (1 hr)
Week 11	Cascades and spreading processes in networks	Lecture (3 hr)
Week 11	Week 11 Computer Laboratory and Tutorial	Computer laboratory (1 hr)
Week 12	Dynamics on Networks	Lecture (3 hr)
Week 12	Week 12 Computer Laboratory and Tutorial	Computer laboratory (1 hr)
Week 13	Student presentations of project	Presentation (4 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.

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. provide solutions to problems through the application of abstract mathematical theory and computational methods.
LO2. transmit information and skills to others through collaborative computing projects.
LO3. summarize, interpret, and differentiate mathematical and computational models in network science.
LO4. evaluate critically the applicability of mathematical models to a given network data.
LO5. create new computational and mathematical models for networks.
LO6. develop new strategies to communicate research results to specialist and non-specialist audiences.
LO7. synthetise and apply mathematical and computational models to problems and data in new contexts.

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 much changes have been made since this unit was last offered.

Additional information

Disclaimer

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.

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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

DATA5441: Networks and High-dimensional Inference

Semester 2, 2025 [Normal day] - 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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links