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.
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
Advanced analytical tasks, such as node/graph classification, link prediction, graph integration, graph alignment, and question answering
Network and knowledge graph evolution
The student must be able to apply advanced graph data analytics methods and techniques theoretically and practically including application in a problem solving.
The type of instruction is organised in accordance with the general instruction methods of the programme, cf. § 17.
The student is expected to spend 30 hours per ECTS, which for this activity means 150 hours.
|Name of exam||Learning and Advanced Analytics on Graph Data|
|Type of exam|
Written or oral exam
|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|
Contact: Study Board for Computer Science via firstname.lastname@example.org or 9940 8854
|Danish title||Læring og avanceret analyse af graf data|
|Language of instruction||Danish|
|Location of the lecture||Campus Aalborg|
|Responsible for the module|
|Study Board||Study Board of Computer Science|
|Department||Department of Computer Science|
|Faculty||The Technical Faculty of IT and Design|