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COMP5313: Large Scale Networks (2019 - Semester 1)

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Unit: COMP5313: Large Scale Networks (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: School of Computer Science
Unit Coordinator/s: Seneviratne, Suranga
Session options: Semester 1
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Brief Handbook Description: 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.
Assumed Knowledge: Algorithmic skills (as expected from any IT graduate). Basic probability knowledge.
Lecturer/s: Seneviratne, Suranga
Tutor/s: Omid Tavallaie

Tham Nguyen
Timetable: COMP5313 Timetable

Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.

Attribute Development Method Attribute Developed
Students will be explained through illustrative examples and real-case situations observable by themselves the impact of these properties in the exchange of information in the world wide web or in social networks. The students will learn and apply thoroughly the different properties inherent to graph theory and sociology that impact the performance and behavior of communicating activities. Such properties made applicable to other situations and examples will help the students develop skills to transpose these properties in other contexts, analyse theoretically their impact, and draw some conclusions on the resulting performance and behavior of various complex communication networks. (1) Maths/ Science Methods and Tools (Level 4)
The task and assignment in the unit give students opportunities to identify, integrate and synthesise knowledge on networks and algorithms to solve problems under constraints.
Assignments will give students the opportunity to develop programming skills by writing social or analytical algorithms and to develop research skills by allowing them to explore and criticise research articles in sociology, economy or graph theory and their applications to information technologies.
(2) Engineering/ IT Specialisation (Level 4)
The students will be given various complex problems that they will need to solve, requiring research of the appropriate background information and research bibliography using resources such as the university library and the Internet. Students will also be required to understand different types of information and its representation and use in complex networks, and will be exposed to standards that ensure the consistency and quality of such information. (6) Communication and Inquiry/ Research (Level 4)

For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table 2018.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

(6) Communication and Inquiry/ Research (Level 4)
1. On successful completion of this unit, a student should be able to, interpret the fundamental structures, dynamics and resource distribution in such models.
(2) Engineering/ IT Specialisation (Level 4)
2. On successful completion of this unit, a student should be able to, explain key factors that impact the accuracy and speed of information dissemination and aggregation.
3. On successful completion of this unit, a student should be able to evaluate the asymptotic complexity and accuracy of graph algorithms.
4. On successful completion of this unit, a student should be able to, describe various types of network models in different contexts like computer science, society or markets
(1) Maths/ Science Methods and Tools (Level 4)
5. On successful completion of this unit, a student should be able to identify and assess accurately the role of networks in number of physical settings.
6. On successful completion of this unit, a student should be able to students identify and describe the technical issues that affect the dissemination of information in a network.
7. On successful completion of this unit, a student should be able analyse probabilistically the relations between communicating entities of a network.
8. On successful completion of this unit, a student should be able to, analyze the stochastic methods necessary to evaluate the convergence of various algorithms.
9. On successful completion of this unit, a student should be able to, recognise probabilistic solutions to problems that have no deterministic solutions and apply them thoroughly.
10. On successful completion of this unit, a student should be able have compare experimentally and theoretically the adequacy of different probabilistic solutions.
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Assignment 1 No 20.00 Week 6 2, 3, 6, 7, 8, 9,
2 Mid-term Quiz No 20.00 Week 7 1, 2, 3, 5, 6, 7, 9,
3 Assignment 2 No 20.00 Week 10 1, 3, 4, 9, 10,
4 Final Exam No 40.00 Exam Period 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
Assessment Description: 1. Assignment 1: Solving problems [20%; due week 6]. Covers outcomes 2, 3, 4, 5, 6 and 7.

2. Midterm Quiz: Answering questions in labs [20%; due week 7]. Covers outcomes 1, 2, 3, 4, 5, 7 and 10.

3. Assignment 2: One of these two 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

[20%; due week 10]. Covers outcomes 5, 7, 8, 9 and 10.

4. Final exam [40%; due exam period] Covers all outcomes.

5. Mark summation: There may be statistically and educationally defensible methods used when combining the marks from each component to ensure consistency of marking between markers, and alignment of final grades with grade descriptors.

6. Penalties for lateness: 5% per day according to the Clause 7A of the assessment procedures.

E.g. An assignment that would normally get 9/10 and is 2 days late loses 10% of the full 10 marks,i.e. new mark = 8/10

An assignment that would normally get 5/10 and is 5 days late loses 25% of the full 10 marks, i.e. new mark = 2.5/10

Assignments more than 10 days late get 0.

7. Similarity detection:The University has authorised and mandated the use of text-based similarity detecting software Turnitin for all text-based written assignments.
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Minimum Pass Requirement It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.
Policies & Procedures: IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Other policies

See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Prescribed Text/s: Note: Students are expected to have a personal copy of all books listed.
Recommended Reference/s: Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 1 Lecture: Introduction (importance of today's networks, BFS, connected component...)
Week 2 Lecture: Graph Ties
Tutorial: Connected - How Kevin Bacon cured cancer by A. Talas
Week 3 Lecture: Network Evolution
Tutorial: Network properties (betweenness, triadic closure...)
Week 4 Lecture: Structural Balance
Tutorial: Manipulation of complex networks
Week 5 Lecture: Structure of Web and Hubs & authorities
Tutorial: Twitter interactions
Week 6 Lecture: Google's PageRank algorithm
Tutorial: Computing the popularity of Web pages
Assessment Due: Assignment 1
Week 7 Lecture: Information Cascades and Power Laws
Assessment Due: Mid-term Quiz
Week 8 Lecture: Small World Phenomenon
Lab: Visualization of complex networks
Week 9 Lecture: Peer to Peer Networks
Week 10 Lecture: Epidemic Spreading & Gossip-based Protocols
Other: Student Mini Project (Assignment 2) Presentations
Tutorial: Power law distribution and decentralized search
Assessment Due: Assignment 2
Week 11 Lecture: Game Theory for Networks (Guest Lecture)
Week 12 Lecture: Recent Developments in Networks: Graph Embeddings and Convolutions
Week 13 Lecture: Review
Exam Period Assessment Due: Final Exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Master of Complex Systems 2020, 2017, 2018, 2019
Bachelor of Advanced Computing/Bachelor of Commerce 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science (Health) 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science (Medical Science) 2018, 2019, 2020
Bachelor of Advanced Computing (Computational Data Science) 2018, 2019, 2020
Bachelor of Advanced Computing (Computer Science Major) 2018, 2019, 2020
Bachelor of Advanced Computing (Information Systems Major) 2018, 2019, 2020
Bachelor of Advanced Computing (Software Development) 2018, 2019, 2020
Bachelor of Computer Science and Technology (Honours) 2015, 2016, 2017
Bachelor of Information Technology 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Arts 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Commerce 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Medical Science 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Science 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Laws 2015, 2016, 2017
Graduate Certificate in Information Technology 2015, 2016, 2017, 2018, 2019, 2020
Graduate Certificate in Information Technology Management 2015, 2016, 2017, 2018, 2019, 2020
Graduate Diploma in Computing 2015, 2016, 2017, 2018, 2019, 2020
Graduate Diploma in Information Technology 2015, 2016, 2017, 2018, 2019, 2020
Graduate Diploma in Information Technology Management 2015, 2016, 2017, 2018, 2019, 2020
Graduate Certificate in Information Technology (till 2014) 2014
Graduate Diploma in Complex Systems 2017, 2018, 2019, 2020
Graduate Diploma in Information Technology (till 2014) 2014
Master of Information Technology 2015, 2016, 2017, 2018, 2019, 2020
Master of Information Technology Management 2015, 2016, 2017, 2018, 2019, 2020
Master of IT/Master of IT Management 2015, 2016, 2017, 2018, 2019, 2020
Master of Information Technology (till 2014) 2014

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(6) Communication and Inquiry/ Research (Level 4) Yes 10%
(2) Engineering/ IT Specialisation (Level 4) Yes 32%
(1) Maths/ Science Methods and Tools (Level 4) Yes 58%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.