### EE 766 – RANDOM GRAPHS

**Semester :** Spring 2018

**Instructor :** Prof Nikhil Karamchandani

**Motivation :** This course dealt with probabilistic graph models, which can be generally used to model a variety of networks, and then study their properties.

**Course Content :**

- Basic random graph models: Erdos-Renyi graphs, structural properties, phase transition
- Other random graph models: Random geometric graphs, Small-world graphs

**Prerequisites :**

Having a good command on probability and probabilistic inequalities will help you substantially. So it would be wise to do this course after having completed EE 325.

**Feedback on lectures :**

The lectures were very much to the point. In each of the lectures, a proof for one property was discussed. The content might get purely mathematical in most lectures, involving heavy usage of approximations and manipulations to obtain the final result. So be wary!

Even though the course had 1.5 hours slots, it was very common that the lecture got over with roughly 20 minutes to spare. The professor would ensure that the doubts, if any, get solved in the class itself and wouldn’t mind taking a diversion to solve the doubts if needed. The professor always uses the board to teach and thus it would always be beneficial to attend the classes.

**Feedback on Exams :**

The course had only a midsem, held in the second half of the semester before the course project presentations. The questions were very related to what was taught in the class. One or two questions were on the tougher side, to test the understanding and solving capabilities.

**Grading and Difficulty level:**

Assignments (2 or 3 in number)- Coding + Theory – 30%

Midsem – 40 %

End term project – 30% (involves presenting a paper in the domain, in teams of 2)

Grading was pretty lenient for the lower grades. It might become tough for the higher grades. Kindly refer to ASC as well. It is easy to get to a decent grade if you put in minimal efforts.

**Attendance :**

Attendance is not mandatory. However, since the class size is pretty small, the professor might keep track of the class participation which could reflect in the overall grading. Attendance for the projects presentations is compulsory and carries marks.

**Books :**

- Bollobas, Random Graphs, Cambridge 2001.
- Frieze and M. Karonski, Introduction to Random Graphs, Cambridge 2016.

**Future direction :** This is a good introduction to random graph models. The project presentations also help you understand the variety of applications this topic has. Based on your interest or application, you can dig in deeper in that direction.

**Reviewed by :** Sravan Patchala (sravps7@gmail.com)