Thesis Topic Details
Topic ID: |
786 | |
Title: |
Community Structure Detection in Large Networks | |
Supervisor: |
Wayne Wobcke | |
Research Area: |
Simulation, Artificial Intelligence, Health informatics | |
| Associated Staff | ||
|---|---|---|
Assessor: |
Mike Bain | |
| Topic Details | ||
Status: |
Active | |
Type: |
R & D | |
Programs: |
SE | |
Group Suitable: |
No | |
Industrial: |
No | |
Pre-requisites: |
-- | |
Description: |
Community structure is common in many large complex networks, especially social networks, the Internet, networks of organisations, and biological networks. Detecting community structure is a difficult task and there is no single algorithm that is suitable for every context. Several datasets of large complex networks are available, and recent interest in large online social networks has spawned several new algorithms for detecting community structure. Many methods are expanded from fundamental clique-based algorithms, while others use more sophisticated processes. Comparisons of different methods in different contexts (such as the size or density of a network) is still widely avoided and is an important gap in network science. | |
Comments: |
Co-supervised with Adam Dunn from Center for Health Informatics. | |
| Past Student Reports | ||
| No Reports Available. Contact the supervisor for more information.
Check out all available reports in the CSE Thesis Report Library. NOTE: only current CSE students can login to view and select reports to download. |
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