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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

COMP5313: Large Scale Networks

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.

Details

Academic unit Computer Science
Unit code COMP5313
Unit name Large Scale Networks
Session, year
? 
Semester 1, 2021
Attendance mode Normal evening
Location Remote
Credit points 6

Enrolment rules

Prohibitions
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None
Prerequisites
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None
Corequisites
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None
Assumed knowledge
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Algorithmic skills gained through units such as COMP2123 or COMP2823 or COMP3027 or COMP3927 or COMP9007 or COMP9123 or equivalent. Basic probability knowledge.

Available to study abroad and exchange students

No

Teaching staff and contact details

Coordinator Lijun Chang, lijun.chang@sydney.edu.au
Type Description Weight Due Length
Final exam (Open book) Type C final exam hurdle task Online Final exam
Online Final Exam.
40% Formal exam period 1.5 hours
Outcomes assessed: LO1 LO10 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Assignment 1
15% Week 06 n/a
Outcomes assessed: LO2 LO3 LO6 LO7 LO8 LO9
Tutorial quiz Mid-term quiz
Answer multiple-choice questions online during Week 7's tutorial time
15% Week 07
Due date: 22 Apr 2021
50 Minutes
Outcomes assessed: LO1 LO2 LO3 LO5 LO6 LO7 LO9
Assignment Assignment 2
submit a 4-6 pages report
30% Week 12 n/a
Outcomes assessed: LO1 LO10 LO9 LO4 LO3
hurdle task = hurdle task ?
Type C final exam = Type C final exam ?

1. Assignment 1: Solving problems

2. Midterm Quiz: Answering multiple-choice questions online during Week 7’s tutorial time

3. Assignment 2: One of these three tasks:

a) Writing a short (4-6 pages) research paper exploring a research topic related to the course in LaTeX and presenting the related work and an analysis of this topic.

b) Programming an algorithm related to the course in C/C++, Java or Python and making a demo of it. Write a report on your findings (4-6 pages).

c) Analyses a real word graph dataset and identify interesting properties of the structure and the dynamics of the graph. Write a report on your findings (4-6 pages).

All three tasks includes doing a short presentation in the class on your findings and conclusions

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 sydney.edu.au/students/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:

Late submission of the midterm quiz will get zero mark. Late project presentation for assignment 2 will get zero mark for the presentation part.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 Introduction (importance of today's networks, BFS, connected component...) (2 hr) LO4 LO5
Week 02 Graph ties (2 hr) LO1
Tutorial: Connected - how Kevin Bacon cured cancer by A. Talas (1 hr) LO4 LO6
Week 03 Structural Balance and Evolution (2 hr) LO7
Tutorial: Manipulation of complex networks (NetworkX) (1 hr) LO1
Week 04 Community Detection (2 hr) LO3
Tutorial: Twitter interaction (1 hr) LO1 LO3 LO7
Week 05 Structure of web and hubs and authorities (2 hr) LO3 LO8
Tutorial: Network properties (betweenness, triadic closure...) (1 hr) LO4 LO5
Week 06 Google's PageRank algorithm (2 hr) LO3 LO8
Tutorial: Computing the popularity of web pages (1 hr) LO3 LO8
Week 07 Information cascades and power laws (2 hr) LO2 LO6
Week 08 Small world phenomenon (2 hr) LO9
Tutorial: Visualization of complex networks (1 hr) LO4
Week 09 Peer to peer networks (2 hr) LO9 LO10
Tutorial: Power law distribution and decentralized search (1 hr) LO2 LO6 LO9 LO10
Week 10 Epidemic spreading and gossip-based protocols (2 hr) LO9 LO10
Week 11 Project Presentation (3 hr) LO1 LO3 LO4 LO9 LO10
Week 12 Recent developments in networks: graph embeddings and neural networks (2 hr) LO4
Week 13 Review (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10

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 on the Library eReserve link available on Canvas.

D. Easly and J. Kleinberg, Networks, Crowds and Markets - Reasoning about a Highly Connected World. Cambridge University Press, 2010. 978-0-521-19533-1.

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. 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’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
No 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.