2025 (Current Year) Faculty Courses School of Computing Department of Computer Science Graduate major in Artificial Intelligence
Complex Networks
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Tsuyoshi Murata
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Class
- -
- Course Code
- ART.T462
- Number of credits
- 200
- Course offered
- 2025
- Offered quarter
- 4Q
- Syllabus updated
- Mar 31, 2025
- Language
- English
Syllabus
Course overview and goals
This course is for the abilities of understanding and analyzing network structures of
complex systems. We study from the viewpoints of network metrics, algorithms, models and
processes.
This course aims at the following three.
1) Study of basic concepts of network structures
2) Practice of network analysis with tools
3) Understand examples of applications of complex networks in various fields
Course description and aims
The goal of this course is to obtain the following abilities.
1) Understanding basic metrics of network structures and computing them for given networks
2) Understanding basic algorithms of network structures
3) Understanding models for network generation and simulating simple ones
4) Understanding the processes on networks such as epidemics
Keywords
Complex networks, Graph theory, Mathematical models
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The lecture will be based on slides and other course material on an overview of complex networks and their analysis.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | introduction | Report assignment (given during the lecture) |
| Class 2 | tools for analyzing networks | Report assignment (given during the lecture) |
| Class 3 | fundamentals (1) mathematics of networks | Report assignment (given during the lecture) |
| Class 4 | fundamentals (2) measures and metrics | Report assignment (given during the lecture) |
| Class 5 | fundamentals (3) the large-scale structure of networks | Report assignment (given during the lecture) |
| Class 6 | network algorithms (1) representation | Report assignment (given during the lecture) |
| Class 7 | network algorithms (2) matrix algorithms | Report assignment (given during the lecture) |
| Class 8 | network algorithms (3) graph partitioning | Report assignment (given during the lecture) |
| Class 9 | network models (1) random graphs | Report assignment (given during the lecture) |
| Class 10 | network models (2) network formation | Report assignment (given during the lecture) |
| Class 11 | network models (3) small-world model | Report assignment (given during the lecture) |
| Class 12 | processes on networks (1) percolation | Report assignment (given during the lecture) |
| Class 13 | processes on networks (2) epidemics | Report assignment (given during the lecture) |
| Class 14 | machine learning and networks (network embedding, graph neural network) | Report assignment (given during the lecture) |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
１． Networks (Second Edition), M. E. J. Newman, Oxford University Press
Reference books, course materials, etc.
１． Networks, Crowds, and Markets, D. Easley and J. Kleinberg, Cambridge University Press
Evaluation methods and criteria
Students' course scores are based on quizzes (100%).
Related courses
- ART.T455 ： Modeling of Discrete Systems
- ART.T451 ： Mathematics of Discrete Systems
Prerequisites
Linear algebra and calculus at the undergraduate level is required for taking this course.
Other
For more information, please refer to the following site.
http://www.net.c.titech.ac.jp/lecture/cn/