2019 Complex Networks

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Academic unit or major
Graduate major in Artificial Intelligence
Instructor(s)
Murata Tsuyoshi 
Course component(s)
Lecture
Day/Period(Room No.)
Mon3-4(G115,W833)  Thr3-4(G115,W833)  
Group
-
Course number
ART.T462
Credits
2
Academic year
2019
Offered quarter
4Q
Syllabus updated
2019/3/18
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

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

Student learning outcomes

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 that will be developed

Intercultural skills Communication skills Specialist 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/Required learning

  Course schedule Required learning
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)
Class 15 summary Report assignment (given during the lecture)

Textbook(s)

1. Networks: An Introduction, M. E. J. Newman, Oxford University Press

Reference books, course materials, etc.

1. Networks: An Introduction, M. E. J. Newman, Oxford University Press

Assessment criteria and methods

Students' course scores are based on quizzes (80%) and reports (20%).

Related courses

  • ART.T455 : Modeling of Discrete Systems
  • ART.T451 : Mathematics of Discrete Systems

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Linear algebra and calculus at the undergraduate level is required for taking this course.

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