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Unit outline_

DATA5441: Networks and High-dimensional Inference

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

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) Lamiae Azizi, lamiae.azizi@sydney.edu.au
Eduardo Goldani Altmann, eduardo.altmann@sydney.edu.au
Type Description Weight Due Length
Assignment Final exam
Written exam
40% Formal exam period 2h
Outcomes assessed: LO1 LO3 LO4 LO5
Assignment Tutorial and lab assignments
Coding and problem-solving.
30% Multiple weeks 1000 words
Outcomes assessed: LO4 LO5
Presentation Project presentation
Group oral presentation
15% Week 13 20 minutes
Outcomes assessed: LO2 LO5 LO6 LO7
Assignment Project Report
See Canvas for more details
15% Week 13 3000 words
Outcomes assessed: LO2 LO7 LO5

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.

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 Lecture (3 hr) LO1 LO3 LO4
Week 1 Computer Laboratory and Tutorial Computer laboratory (1 hr) LO5 LO6
Week 02 Centrality measures Lecture (3 hr) LO1 LO3
Week 2 Computer Laboratory and Tutorial Computer laboratory (1 hr) LO5 LO6 LO7
Week 03 Random Graph Models Lecture (3 hr) LO1 LO3 LO5 LO7
Week 3 Computer Laboratory and Tutorial Computer laboratory (1 hr) LO5 LO6 LO7
Week 04 Random graphs vs. complex networks Lecture (3 hr) LO1 LO3 LO4 LO5 LO7
Week 4 Computer Laboratory and Tutorial Computer laboratory (1 hr) LO5 LO6 LO7
Week 05 Mechanistic network models Lecture (3 hr) LO1 LO3 LO4 LO5
Week 5 Computer Laboratory and Tutorial Computer laboratory (1 hr) LO5 LO6 LO7
Week 06 Inference: Importance sampling Lecture (3 hr) LO1 LO2 LO3 LO5 LO7
Week 6 Computer Laboratory and Tutorial Computer laboratory (1 hr) LO5 LO6 LO7
Week 07 Community detection in networks Lecture (3 hr) LO1 LO2 LO3 LO4 LO5
Week 7 Computer Laboratory and Tutorial Computer laboratory (1 hr) LO5 LO6 LO7
Week 08 Methods for high-dimensional inference Lecture (3 hr) LO1 LO2 LO3 LO4 LO7
Week 8 Computer Laboratory and Tutorial Computer laboratory (1 hr) LO5 LO6 LO7
Week 09 Inference of large scale structures in networks Lecture (3 hr) LO1 LO2 LO3 LO4 LO7
Week 9 Computer Laboratory and Tutorial Computer laboratory (1 hr) LO5 LO6 LO7
Week 10 Network Resilience Lecture (3 hr) LO1 LO2 LO3 LO4
Week 10 Computer Laboratory and Tutorial Computer laboratory (1 hr) LO5 LO6 LO7
Week 11 Cascades and spreading processes in networks Lecture (3 hr) LO2 LO3 LO4 LO7
Week 11 Computer Laboratory and Tutorial Computer laboratory (1 hr) LO5 LO6 LO7
Week 12 Dynamics on Networks Lecture (3 hr) LO1 LO2 LO3 LO4
Week 12 Computer Laboratory and Tutorial Computer laboratory (1 hr) LO5 LO6 LO7
Student presentations of project Presentation (4 hr) LO4 LO5 LO6 LO7

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 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. 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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.

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.