Instructor: Fragkiskos Malliaros (John Cagnol, Sarah Lemler and Véronique Letort also participate at the thematic sequence ST2) Email: fragkiskos.me [at] gmail.com Office hours: Right after class (or send me an email and we will find a good time to meet) TA: Abdulkadir Çelikkanat Email: abdcelikkanat [at] gmail.com Piazza: piazza.com/centralesupelec/winter2019/st2/home
- Dates: December '18 - January '19
Which people should we immunize, to prevent an epidemic (e.g., Ebola) as fast as possible? Which users should we target to in order to achieve an effective promotion campaign for our product? How do opinions or rumors get formed and propagate in social media (e.g., Twitter)? The modeling of propagation in various contexts (populations, materials, computer networks, social networks) has seen great progress in its formalization in recent decades. The goal of this class is to familiarize students with the classic techniques that will allow them to reproduce, predict and anticipate the effects of a virus or information propagation in real-life situations. Two main approaches will be considered in this course: propagation in a homogeneous population, leading to continuous modeling; propagation in a structured population with an underlying graph layer. In the latter case, the properties of the propagation phenomenon depend on the graph structure.
|1||December 10||Introduction to propagation modeling and graph analysis||Lecture 1|
|2||December 12||Centrality criteria and link analysis algorithms||Lecture 2|
|3||December 17||Models of information propagation on graphs||Lecture 3|
|4||December 19||Identification of influential spreaders on graphs||Lecture 4|
|5||January 7||Influence maximization in social networks||Lecture 5|
Prerequisites There is no official prerequisite for this course. However, the students are expected to:
Reading material Most of the material of the course is based on the books shown below. Some of the topics covered in the course are also based on research articles.