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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.

Code COMP5313
Academic unit Computer Science
Credit points 6
Assumed knowledge:
Algorithmic skills gained through units such as COMP2123 or COMP2823 or COMP3027 or COMP3927 or COMP9007 or COMP9123 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 outlines

Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.