Graf Representation Learning(INF 515)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| INF 515 | Graf Representation Learning | 2 | 3 | 0 | 0 | 3 | 6 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Elective |
| Course Level | Masters Degree |
| Course Instructor(s) | Günce Keziban ORMAN korman@gsu.edu.tr (Email) |
| Assistant | |
| Objective | This course aims to teach the underlying theory and techniques for transforming graphs—used for data modeling in various fields—into numerical vectors through next-generation representation learning methods. It covers the subject in a broad spectrum, ranging from traditional spectral methods to contemporary Graph Neural Network (GNN) techniques. The primary objective is to equip students with the necessary tools to construct complex systems logic for data analysis and to select the appropriate representation learning technique to solve the problems they encounter. |
| Content |
Introduction and Foundations of Graph Theory Traditional Graph Statistics and Kernel Methods Neighborhood Overlap and Spectral Methods Shallow Node Embeddings and Encoder-Decoder Framework Random Walk Methods and Knowledge Graphs Graph Neural Networks (GNN) and Message Passing Aggregation and Update Methods in GNN Architectures Midterm Exam Graph Pooling and Relation Prediction Applications Efficiency in GNN Applications and Node Sampling Spectral Graph Convolutions and Theoretical Motivations GNN Capacity and Graph Isomorphism Traditional and Deep Generative Graph Models Project Presentation |
| Course Learning Outcomes |
- Ability to handle a complex network as a graph - Ability to transform a graph into numerical vectors suitable for the studied problem |
| Teaching and Learning Methods | Lecture, brainstorming, discussion |
| References |
https://www.cs.mcgill.ca/~wlh/grl_book/files/GRL_Book.pdf http://web.stanford.edu/class/cs224w/ |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Introduction and Foundations of Graph Theory |
| 2 | Traditional Graph Statistics and Kernel Methods |
| 3 | Neighborhood Overlap and Spectral Methods |
| 4 | Shallow Node Embeddings and Encoder-Decoder Framework |
| 5 | Random Walk Methods and Knowledge Graphs |
| 6 | Graph Neural Networks (GNN) and Message Passing |
| 7 | Aggregation and Update Methods in GNN Architectures |
| 8 | Midterm Exam |
| 9 | Graph Pooling and Relation Prediction Applications |
| 10 | Efficiency in GNN Applications and Node Sampling |
| 11 | Spectral Graph Convolutions and Theoretical Motivations |
| 12 | GNN Capacity and Graph Isomorphism |
| 13 | Traditional and Deep Generative Graph Models |
| 14 | Project Presentation |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 2 | 60 |
| Contribution of final exam to overall grade | 1 | 40 |
| Toplam | 3 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 30 |
| Project | 1 | 30 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Make-up | 0 | 0 |
| Toplam | 2 | 60 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | X | |||||
| 2 | X | |||||
| 3 | X | |||||
| 4 | X | |||||
| 5 | X | |||||
| 6 | X | |||||
| 7 | X | |||||
| 8 | X | |||||
| 9 | X | |||||
| 10 | X | |||||
| 11 | ||||||
| 12 | X | |||||
| 13 | X | |||||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 12 | 3 | 36 |
| Working Hours out of Class | 12 | 6 | 72 |
| Assignments | 0 | 0 | 0 |
| Presentation | 0 | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 10 | 10 |
| Project | 1 | 10 | 10 |
| Laboratory | 0 | 0 | 0 |
| Other Applications | 0 | 0 | 0 |
| Final Examinations (including preparation) | 1 | 20 | 20 |
| Quiz | 0 | 0 | 0 |
| Term Paper/ Project | 0 | 0 | 0 |
| Portfolio Study | 0 | 0 | 0 |
| Reports | 0 | 0 | 0 |
| Learning Diary | 0 | 0 | 0 |
| Thesis/ Project | 0 | 0 | 0 |
| Seminar | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Make-up | 0 | 0 | 0 |
| Yıl Sonu | 0 | 0 | 0 |
| Hazırlık Yıl Sonu | 0 | 0 | 0 |
| Hazırlık Bütünleme | 0 | 0 | 0 |
| Total Workload | 148 | ||
| Total Workload / 25 | 5.92 | ||
| Credits ECTS | 6 | ||