Learning and Advanced Analytics on Graph Data

2022/2023

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 examLearning and Advanced Analytics on Graph Data
Type of exam
Written or oral exam
ECTS5
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe 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 titleLæring og avanceret analyse af graf data
Module codeDSNDVK104
Module typeCourse
Duration1 semester
SemesterAutumn
ECTS5
Language of instructionDanish
Empty-place SchemeYes
Location of the lectureCampus Aalborg
Responsible for the module

Organisation

Study BoardStudy Board of Computer Science
DepartmentDepartment of Computer Science
FacultyTechnical Faculty of IT and Design