Course syllabus for Machine learning with graphs

Course syllabus adopted 2025-02-07 by Head of Programme (or corresponding).

Overview

  • Swedish nameMaskininlärning med grafer
  • CodeEEN245
  • Credits7.5 Credits
  • OwnerMPICT
  • Education cycleSecond-cycle
  • Main field of studyComputer Science and Engineering
  • DepartmentELECTRICAL ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 90132
  • Open for exchange studentsYes

Credit distribution

0125 Examination 7.5 c
Grading: TH		 7.5 c

In programmes

Examiner

Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Familiarity with basic probability theory and linear algebra. A foundation in machine learning is required.

Aim

Many real-world data, such as social networks, transaction networks, biological pathways, communication networks, and molecular structures, are inherently graph-structured. Traditional machine learning approaches designed for sequential data (e.g., text) or grid-structured data (e.g., images) often fall short in capturing the intricate relationships and unique properties graph data.

The aim of this course is to equip students with modern machine learning techniques specifically designed for graph-structured data. Students will gain a foundational understanding of core concepts such as graph representation learning and graph neural networks (GNNs), which have become a dominant and fast-growing paradigm for deep learning with graph data.

Through hands-on projects and real-world case studies, the course emphasizes practical problem-solving while grounding students in the essential theoretical principles. By the end of the course, students will be prepared to apply graph-based machine learning techniques to tackle complex challenges across diverse fields.

Learning outcomes (after completion of the course the student should be able to)

• Explain the unique properties of graph-structured data and reflect on the challenges it presents for traditional machine learning methods • Develop and evaluate node embeddings to represent graph data effectively for downstream machine learning tasks • Apply graph neural networks to make make better predictions by leveraging graph-structured data • Construct and optimize various graph neural network architectures, such as convolutional and attention based variants, for real-world applications • Explain and analyze the mathematical principles underlying GNNs, including their expressive power and limitations • Critically assess the performance of graph-based machine learning models • Solve a practical project involving machine learning with graphs, encompassing problem formulation, implementation, evaluation, and interpretation of the results

Content

• Introduction to machine learning with graphs: Fundamental concepts of graph-structured data, its unique properties, and the challenges it presents in machine learning.
• Node embeddings: Techniques to represent nodes as feature vectors that capture the graph structure and node-specific information, enabling downstream machine learning tasks.
• Graph neural networks: Detailed exploration of GNN architectures, including convolutional and attention based variants, and their applications across diverse domains.
• Theory of graph neural networks: Understanding the mathematical principles behind GNNs, their expressive power, and limitations.
• Graph transformers:  Introduction to transformer architectures adapted for graph data, enabling advanced capabilities like long-range dependency modeling.
• Knowledge graphs: Representation and reasoning techniques over knowledge graphs, including their construction, embedding methods, and query processing.
• Deep generative models for graphs: Techniques for generating graph data using deep learning models.

Organisation

The course is comprised of lectures, tutorial sessions, and 1 final take-home project.

Literature

TBD

Examination including compulsory elements

The final grade (TH) is based on scores from the hand-in assignments and final take-home project. The project is mandatory in the sense that it must be passed to pass the course.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.