Augustin Chaintreau (instructor), Xiao Zhu (teaching assistant), Di Li (teaching assistant) Please post any question or inquiry on our Online forum (unless it deals with a personal exception).

What do I need to know to take the class?

The course requires prior knowledge of elementary discrete probability, linear algebra and elementary graph theory, as well as some familiarity with a programming language (either python, C or java). If you would like to refresh these notions, you may consider review the following references.

Homogeneous Markov Chain: Chap.1-3 in P. Bremaud, Markov chains: Gibbs fields, Monte Carlo simulation, and queues (2010) Springer.

Graph Theory: Chap.1 in R. Diestel, Graph Theory (2010), Springer.

Linear Algebra: Matrix, eigenvalues, eigenvectors, in, e.g., G. Strang Introduction to Linear Algebra, Wellesley Cambridge Press (2009).

How will I be graded?

The evaluation will be based on:

about 3~4 problem sets containing mathematical exercices, small programming projects and case studies

participations to 2 data contests during the term

a mid-term exam

a final project with an ambitious research problem addressed

Exact percentage will be decided and announced on the third week (depending on the size and interest of the class)

Should I buy a textbook? Are there mandatory readings?

There is no mandatory books to buy for the class; unfortunately the topics covered in this course are not described in a textbook at the graduate level.

To broaden your vision on this topic, a list of 10 sociological "must read" papers will be given during the class, to read on your own and use in your interpretation and discussion.

The course is more or less self-contained, references are given to allow you to review the material before or after class

You will find references, including links to similar classes, in our
The book Networks, Crowds, and Markets: Reasoning About a Highly Connected World, by D. Easley and J. Kleinberg may be used as a very good introduction to this course (and other topics in the domain). The most relevant parts are I and IV-VI.

How do I hand in late homework?

Please arrange with a TA or Prof. Chaintreau to hand in the hard copy of your homework during an office hours. Programming portions of the homework should be turned in online, as normally done.

## Where and when are the lectures / office hours?

Office-hours(everyday so everyone can come):## Who is in charge?

Augustin Chaintreau (instructor), Xiao Zhu (teaching assistant), Di Li (teaching assistant)

Please post any question or inquiry on our Online forum (unless it deals with a personal exception).

## What do I need to know to take the class?

The course requires prior knowledge of elementary discrete probability, linear algebra and elementary graph theory, as well as some familiarity with a programming language (either python, C or java).

If you would like to refresh these notions, you may consider review the following references.

Markov chains: Gibbs fields, Monte Carlo simulation, and queues(2010) Springer.Graph Theory(2010), Springer.Introduction to Linear Algebra,Wellesley Cambridge Press (2009).## How will I be graded?

The evaluation will be based on:- about 3~4 problem sets containing mathematical exercices, small programming projects and case studies
- participations to 2 data contests during the term
- a mid-term exam
- a final project with an ambitious research problem addressed

Exact percentage will be decided and announced on the third week (depending on the size and interest of the class)## Should I buy a textbook? Are there mandatory readings?

The book Networks, Crowds, and Markets: Reasoning About a Highly Connected World, by D. Easley and J. Kleinberg may be used as a very good introduction to this course (and other topics in the domain). The most relevant parts are I and IV-VI.

## How do I hand in late homework?

Please arrange with a TA or Prof. Chaintreau to hand in the hard copy of your homework during an office hours. Programming portions of the homework should be turned in online, as normally done.