This is the page of the Complex Networks course, part of the Science of Complex Systems Option of the M2 at ENS de Lyon.

The class covers: 1)Fundamentals of Network Science, e.g., Classic random models, centralities, small-world phenomenon, etc. 2)Focus classes on advances topics, e.g., dynamic networks, community detection, machine learning on graphs 3)An introduction to cutting-edge research topics, such as Graph embedding and Graph Convolutional Neural Networks (GCN).

This class is thought to provide a broad overview of current topics in network science. It is grouded in research, with short presentations by researchers in the field introducing their research questions, and a cession for collective reading and commenting of recent scientific articles. The objective of the course is to lead the students to a point where they are able to gain a general understanding of most articles currently published in the field.

The course is composed of 24h of lectures, and 6 hours of tutorial (TP).

- The first two are useful to give you a concrete notion of what dealing with networks can mean, and constitute an introduction to the manipulation of networks through code
- The last one requires you to send us files that we will grade, so don't forget not to spend too much time on the others.

Below is an overview of the class, and slides of presentations. This organization can be subject to changes ! The content is currently the one used in 2019/2020.

- Introduction, Describing Networks: PDF, notebook
- Centralities and Similarities: PDF, Eigenvector Centrality notebook
- Random Graph Models I: ER, Configuration, WS PDF, friendship paradox notebook
*Guest Speakers: Complex Networks seen by researchers*Luisa Di Paola - Lorenza Pacini - Esteban Bautista- Random Graph Models II: Scale Free, Barabasi, Forest Fire, etc. PDF
- Communities and Community Detection PDF
- Dynamic Networks PDF
- Complexifying Complex Networks (Multilayer, higher Order, Spatial, etc.) PDF
- Machine Learning on graphs (Link Prediction, Node classification, etc.) PDF
- Graph Embedding and Graph Convolutional Networks PDF notebook
- Spreading Processes PDF
- Last class: article reading and commenting.
**Note: the last two classes are on the same day**

There will be two grades given in this class:

Last year exam can be found there: Subject - Article.

- One grade for the tutorials' project, representing 40% of the mark. The project should be sent to Rémy Cazabet (remy.cazabet AT univ-lyon1.fr), dates to come.
- One grade for the final exam, representing 60% of the mark.

- Explain in your own terms the interpretation of a definition/equation
- Comment about a proposed definition (a centrality, a community detection algorithm...) by doing a parallel with related definitions/methods we have discussed during the class
- Propose a variant, an improvement of a method introduced in the paper, based on what we have seen during the class
- Write a critical evaluation about some aspect of the article, weaknesses and/or strengths you could identify compared to the state of the art (introduced in the class).

Last year exam can be found there: Subject - Article.