| Academic Year |
2026Year |
School/Graduate School |
Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program |
| Lecture Code |
WSN23502 |
Subject Classification |
Specialized Education |
| Subject Name |
Network Science and Graph Learning |
Subject Name
（Katakana） |
|
Subject Name in
English |
Network Science and Graph Learning |
| Instructor |
LOU YANG |
Instructor
(Katakana) |
ロウ ヤン |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, Second Semester, 3Term |
| Days, Periods, and Classrooms |
(3T) Mon5-8 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face, Online (on-demand) |
This is a lecture-based class, primarily held in person, with online (on demand) sessions available only in special situations, such as severe weather, the instructor’s business trip, or other exceptional circumstances.
The first lesson will be conducted in person.
|
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
E
:
English |
| Course Level |
6
:
Graduate Advanced
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
|
| Keywords |
Complex Networks; Graph Representation Learning; Graph Neural Networks; Network Modeling and Analysis; Relational Data Learning |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
This course introduces the principles of network science and graph learning, focusing on modeling, analyzing, and learning from relational data using modern graph‑based methods, including graph neural networks. |
| Class Schedule |
lesson1 Introduction to Network Science and Graph Learning
lesson2 Graph Fundamentals and Network Representation
lesson3 Structural Properties of Networks
lesson4 Classical Network Models
lesson5 Centrality and Importance Measures
lesson6 Community Detection and Graph Partitioning
lesson7 Graph Laplacians and Spectral Methods
lesson8 Shallow Graph Representation Learning
lesson9 Learning on Multi Relational and Knowledge Graphs
lesson10 Graph Neural Network Foundations
lesson11 Popular GNN Architectures
lesson12 GNNs for Different Learning Tasks
lesson13 Theoretical Perspectives on GNNs
lesson14 Advanced Topics in Graph Learning
lesson15 Graph Generation |
Text/Reference
Books,etc. |
W. L. Hamilton. Graph Representation Learning. Morgan & Claypool, 2020 |
PC or AV used in
Class,etc. |
Text, Handouts, moodle |
| (More Details) |
|
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
(1) Each lesson's materials (e.g., slides provided by the instructor) should be reviewed both before and after class.
(2) The meaning of all English technical terms should be understood before class. |
| Requirements |
|
| Grading Method |
Participation and Assignments: 20%
Final Report: 80% |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
|
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |