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
Lecturer(s)	Eduardo Goldani Altmann, eduardo.altmann@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam	Final exam Written exam	40%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Tutorial and lab assignments Coding and problem-solving.	30%	Multiple weeks	1000 words
Outcomes assessed:
Presentation	Project presentation Group oral presentation	15%	Week 13	20 minutes
Outcomes assessed:
Assignment	Project Report See Canvas for more details	15%	Week 13	3000 words
Outcomes assessed:

Assessment summary

Final exam (40%): theory and general concepts

Assignments (30%): theory and computation

Final project (30%) = presentation (15%) + final report (15%): theory, computation, and critical discussion

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.

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:

Final exam: replacement exam will be provided. Assignments: standard late penalty. Final project: no credit awarded for late submissions.

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

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 Computer Laboratory and Tutorial	Computer laboratory (1 hr)
	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.

This is the first time this unit will be 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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect

Current students

DATA5441: Networks and High-dimensional Inference

Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect