Introduction
This is the page of the Complex Networks course at ENS de Lyon.
This class can be followed by students from different specializations:

• (CompSci) M2 Foundations of Computer Science
• (CompSys) Complex System option (M2 Physics and Biology).

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 3)A focus on Graph Neural Networks (Computer Science students only, after January 6)
Objectives
This class is thought to provide a broad overview of current topics in Network Science. It is grouded 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
The course organisation depends on your specialization: 24h are common to all students (CS+info), 8 hours in January are mandatory only for info students, while 8 hours of practicals are mandatory only for CS students. All students are welcome to attend all classes if they wish.

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 !

I created a Discord channel that you can join if you want to ask questions about the class, projects, etc. link: https://discord.gg/GWrS2KpTJq

Day	Time	Room	Group	Topic	Resources
Tuesday Nov.15	8h00-10h00	F	All	Lecture: Introduction, Describing Networks, Centralities	CheatSheet_intro - CheatSheet_matrices - CheatSheet_centralities - Slides
Tuesday Nov.22	8h00-10h00	F	All	Experiments: Gephi, networkx intro	Practicing: Gephi - Practicing: networkx Project description - Past year projects example - Centrality guess
Tuesday Nov.22	13h30-15h30	F	CompSys	TP+Project	Introduction to Pandas Python datastructures Practical: Scientometrics
Thursday Nov. 24	10h15-12h15	F	All	Lecture: Random Graphs	Lecture - CheatSheet_RandomGraphs - CheatSheet_ScaleFree - CheatSheet_Spatial
Tuesday Nov. 29	08h00-10h00	F	All	Experiments: Random Graphs	Experiments: Random Graphs
Thursday Dec. 1	8h00-10h00	F	CompSys	Project
Thursday Dec. 1	10h15-12h15	F	All	Lecture: Spreading processes + Experiments: Spreading processes	Experiments: Diffusion Lecture CheatSheet Diffusion
Tuesday Dec. 6	08h00-10h00	F	All	Lecture: Communities+ ML classic	Lecture CheatSheet Communities CheatSheet Machine Learning
Thursday Dec. 8	8h00-10h00	F	CompSys	Project
Thursday Dec. 8	10:15-12:15	F	All	Experiments: Communities	Experiments: Communities Modularity correction
Tuesday Dec. 13	08h00-10h00	F	All	Lecture: Dynamic Networks + dynamic communities	Lecture CheatSheet Dynamic
Thursday Dec. 15	8h00-10h00	F	CompSys	Project
Thursday Dec. 15	10h15-12h15	F	All	A. Guille: Representation Learning 1
Tuesday Jan. 3	8h00-10h00	F	All	Experiments: Dynamic Networks	Experiments: Dynamic
Thursday Jan. 5	10h15-12h15	F	All	A. Guille: Representation Learning 2
Friday Jan. 6	13h30-15h30	L	CompSys	Final Exam Complex Systems
Tuesday Jan. 11	8h00-10h00	B1	CompSci	A. Guille: GNN1
Thursday Jan. 13	10h15-12h15	B1	CompSci	A. Guille: GNN2
Tuesday Jan. 17	8h00-10h00	B1	CompSci	A. Guille: GNN3
Thursday Jan. 19	10h15-12h15	B1	CompSci	A. Guille: GNN4
Thursday Jan. 26	10h15-12h15	B1	CompSci	Final Exam Computer Science

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)


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

At the demand of some students, I add the possibility for all students to postpone the date of delivery of the project:

• If you send the project before December 18, I'll give you the grades before January 2. I'll of course take into account the short time you had to prepare the project
• Else, you can send your project up to January 6. I will not penalize in the grades, although I will be a little less lenient on the report's format (It should not look like a draft made in a hurry).

There will be two grades given in this class, with coefficients and exams depending on your master:

Complex System Students

• A network analysis project. Send before December 18, 23h59. (60%)
• A final Exam (January 6, 40%). All documents allowed (no computers)Example of a previous year

Computer Science Students

• A short project, application of experiments. Send before December 18, 23h59. (30%) 50%
• A final Exam (End of January, (70%) 50%)

The page of last year's course can be found there: 2021/2022.