Networks or graphs are made of entities and the relations between them; they represent interacting systems.
The entities constitute the nodes (or vertices) of the network and the relations give network’s edges (or links).
Networks are graphs that are “large” and “sparse”. Complex networks are networks which (unlike simple graphs such as grids) have “nontrivial topological features”.
In this course we study different problems related to networks such as node importance and centrality, community detection, network generative models (random graph model and preferential attachment, deep graph generative models) as well as graph Graph Neural Networks and Machine Learning on graphs.
Instructor: Reza Rezazadegan
Shiraz University, Department of Mathematics and Computer Science, Spring 2025
Course webpage: https://dreamintelligent.com/complex-network-analysis-2025/
Course Github: https://github.com/rezareza007/networks
Last year’s course: https://dreamintelligent.com/complex-network-analysis-course/
TA: M. Ayubi @Martin_Auobii
Course texts
Albert-Laszlo Barabasi, Network Science
Easley and Kleinberg, Network, Crowds and Markets
Hamilton, Graph Representation Learning
Jure Leskovec, Stanford CS224W: Machine Learning with Graphs
Other useful texts:
Newman, Networks, An Introduction
Zinoviev, Complex Network Analysis in Python
Course evaluation
- Recitation (3 points)
- Small project (4 points)
- Presentation of a topic (6 pints). Presentation topics are mentioned below.
- Data analysis project (7 points)