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Curricular information is subject to change
Students will have
- Learned how to characterise a network according to a number of measures.
- Compute network measures, with the assistance of appropriate computational tools.
- Carry out steps to analyse network data, from processing of raw data to extraction of network structure.
- Apply network analysis in an application such as recommendation, information diffusion etc.
Students will study the following topics:
- Measures of network characteristics, including clustering, degree distribution etc.
- Algorithms to find dense clusters in networks.
- Community finding in social networks.
- Information diffusion in networks.
- Trust networks and their application in recommender systems.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Practical | 36 |
Autonomous Student Learning | 140 |
Total | 200 |
Students taking this module should be experienced programmers. It is also a prerequisite to have a minimum of two years industrial software engineering experience. Please contact Dr. Mel Ó Cinnéide if you are eligible and wish to register for this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment: Programming/Data analysis assignments | Coursework (End of Trimester) | n/a | Graded | No | 60 |
Class Test: In class examination. (Online in 2021) | 2 hour End of Trimester Exam | n/a | Graded | No | 40 |
Remediation Type | Remediation Timing |
---|---|
Repeat | Within Two Trimesters |
• Feedback individually to students, post-assessment
Not yet recorded.