Learning and Advanced Analytics on Graph Data
2024/2025
Content, progress and pedagogy of the module
Disclaimer. This is an English translation of the module. In case of discrepancy between the translation and the Danish version, the Danish version of the module is valid.
Learning objectives
Knowledge
The student must acquire knowledge and skills in advanced graph data analytics, for example:
Predictive analysis on diverse types of graphs, such as networks, spatial networks, and knowledge graphs
Graph representation learning and graph embeddings
Graph kernels
Probabilistic models
Advanced analytical tasks, such as node/graph classification, link prediction, graph integration, graph alignment, and question answering
Network dynamics
Network and knowledge graph evolution
Information diffusion
Skills
- Be able to demonstrate knowledge of advanced graph data
analytics methods and techniques
- Be able to select relevant concepts and techniques for a given
problem within advanced graph data analytics
- Be able to use correct notation and terminology within advanced graph data analytics
Competences
The student must be able to apply advanced graph data analytics methods and techniques theoretically and practically including application in a problem solving.
Type of instruction
The type of instruction is organised in accordance with the general instruction methods of the programme, cf. § 17.
Extent and expected workload
The student is expected to spend 30 hours per ECTS, which for this activity means 150 hours.
Exam
Exams
| Name of exam | Learning and Advanced Analytics on Graph Data |
| Type of exam | Written or oral exam |
| ECTS | 5 |
| Assessment | 7-point grading scale |
| Type of grading | Internal examination |
| Criteria of assessment | The criteria of assessment are stated in the Examination Policies and Procedures |
Additional information
Contact: Study Board for Computer Science via cs-sn@cs.aau.dk or 9940 8854
Facts about the module
| Danish title | Læring og avanceret analyse af graf data |
| Module code | DSNDVK104 |
| Module type | Course |
| Duration | 1 semester |
| Semester | Autumn
|
| ECTS | 5 |
| Language of instruction | Danish |
| Empty-place Scheme | Yes |
| Location of the lecture | Campus Aalborg |
| Responsible for the module |
Organisation
| Education owner | Master of Science (MSc) in Data Science and Machine Learning |
| Study Board | Study Board of Computer Science |
| Department | Department of Computer Science |
| Faculty | The Technical Faculty of IT and Design |
