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Unit of study_
COMP4313: Large Scale Networks
2025 unit information
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.
Unit details and rules
Managing faculty or University school:
Engineering
| Study level | Undergraduate |
|---|---|
| Academic unit | Computer Science |
| Credit points | 6 |
|
Prerequisites:
?
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DATA3888 or COMP3888 or COMP3988 or CSEC3888 or SOFT3888 or ENGG3112 or SCPU3001 |
|---|---|
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Corequisites:
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Enrolment in a thesis unit INFO4001 or INFO4911 or INFO4991 or INFO4992 or AMME4111 or BMET4111 or CHNG4811 or CIVL4022 or ELEC4712 or COMP4103 or SOFT4103 or DATA4103 or ISYS4103 |
| Prohibitions:
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COMP5313 |
| Assumed knowledge:
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A major in a computer science area. Algorithmic skills gained through units such as COMP2123 or COMP2823 or COMP3027 or COMP3927 or equivalent. Basic probability knowledge |
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.
Unit availability
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 1 2025
|
Normal evening | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.
