Introduction
This is the page of the Complex Networks course at ENS de Lyon., in the Complex System option

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, graph algorithmic, community detection. Teachers for lectures are Rémy Cazabet and Pierre Borgnat. Practicals are independently teached by Juliana Du.

Objectives
This class is thought to provide a broad overview of current topics in Network Science. It is grounded in research. 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.

Overview of the course
There are 24h of lectures and 8h of practicals. The lectures by me (Rémy Cazabet) are divided in 4 parts of 4h each, composed of 2 hours of lectures and 2 hours of experiments. The practicals are done in python, using notebooks. The course is given in English, unless all students are French speakers.

Below is an overview of the courses and practicals. As the classes are done, I'll add my presentation slides, cheatsheets, some notebooks, etc.
This organization can be subject to changes !

Day	Time	Room	Teacher	Topic	Resources
Thursday Sep. 14	13h30-15h30	F	Rémy Cazabet	Lecture: Introduction, Describing Networks, Centralities	CheatSheet_intro - CheatSheet_matrices - CheatSheet_centralities - Slides
Thursday Sep.21	10h15-12h15	F	Rémy Cazabet	Experiments: Gephi, networkx intro	TP1 - TP2
Thursday Sep.28	10h15-12h15	F	Rémy Cazabet	Lecture: Random Graphs	Lecture - CheatSheet_RandomGraphs - CheatSheet_ScaleFree - CheatSheet_Spatial
Thursday Oct. 5	10h15-12h15	F	Rémy Cazabet	Experiments: Random Graphs	TP
Thursday Oct. 12	10h15-12h15	F	Pierre Borgnat	Spreading processes ; Dynamic on networks
Thursday Oct. 19	10h15-12h15	F	Pierre Borgnat	Graph Signal processing
Thursday Oct. 26	10h15-12h15	F	Rémy Cazabet	Lecture: Communities + ML classic	Lecture CheatSheet communities CheatSheet Machine Learning
Thursday Nov. 9 - Déplacé	15h45-17h45	F	Pierre Borgnat	Representation Learning for graphs ; embeddings
Thursday Nov. 16	15h45-17h45	F	Rémy Cazabet	Experiments: Communities	TP communities
Thursday Nov. 23	15h45-17h45	F	Rémy Cazabet	Lecture: Dynamic of Networks + dynamic communities	Lecture CheatSheet Dynamic
Thursday Nov. 30	15h45-17h45	M1 105	Rémy Cazabet	Experiments: Dynamic of networks	TP temporal
Thursday Dec. 7	15h45-17h45	M1 103	Rémy Cazabet	Graph Convolutional Networks	Lecture

Day	Time	Room	Teacher	Topic	Resources
Thursday Oct. 19	8h00-10h00	F	Juliana Du	Practicals + Project	Scientometrics practicals
Tuesday Nov. 28	8h00-10h00	F	Juliana Du	Project	Project
Tuesday Dec. 5	8h00-10h00	M1 103	Juliana Du	Project
Tuesday Dec. 12	8h00-10h00	M1 105	Juliana Du	GNN + project	Code example TP A toy dataset of student friendship network Download An airport dataset with approximate population of the airport city Download

Small networks
Small network Game Of Thrones(.graphml)
Airports with location and country(.graphml)

Dataset collections
There are many network dataset repositories available on the web, here is for instance a collection of collection of networks: network-datasets
A recent addition to this list is Netzschleuder repository, which has the advantage of allowing to load a graph directly from its name. Here is a minimal working example, to load the openflights dataset:

from io import BytesIO
from zipfile import ZipFile
from urllib.request import urlopen
address="https://networks.skewed.de/net/openflights/files/openflights.csv.zip"
resp=urlopen(address)
zipfile = ZipFile(BytesIO(resp.read()))
strf=zipfile.read("edges.csv").decode().split("\n")
g= nx.parse_edgelist(strf,delimiter=",",data=False)

More generally, if you want to find data of interest, I provide here some tips to find them.

For tutorials and the experimental part of lectures, you need to use some softwares, detailed below.

Gephi

Gephi is a software for basic graph manipulation and visualization. Although you can't do much in term of graph analysis, it is really convenient to explore and visualize graphs of small to medium size ( < 1000 nodes).
It can be donwloaded there : Gephi.

Python

Most of the experiments are done in python. If you're not familiar with this language, there are numerous tutorials on the web. A good one for instance is from w3schools. You don't need to install anything, since you can do all the experiments using an online notebook such as Google colab. If you prefer to use a local installation on your computer, here is a list of packages we will use. Note that some of them are only available with pip, and not anaconda. If you're using anaconda, you can neverthless use them, using the pip command (pip install package_name).

• networkx. Generic network analysis
• notebook. Jupyter notebook
• cdlib. Community detection
• tnetwork. Temporal networks
• scikit-learn. Machine learning/Data mining
• seaborn. ploting library

There will be two grades given in this class:

• A network analysis project. Send before December 17, 23h59. (40%)
The description of the project is available there: Project
You can work in groups of 2 or 3 students. You must clarify what each student has done (graes can be personnalized).
You can find some examples of previous years' projects there. (Please send your project as a notebook, I converted them here as PDF for convenience)
• A final Exam (January 6, 60%). All documents allowed (no computers). This exam covers classes by all teachers. You can find the exam of the previous year there. Note that the program was not exactly the same, but it gives an idea of the kind of questions you can have on my part.