# Statistical Network Analysis (WS 2018/19)

Lecture for Masters students by Debarghya GhoshdastidarThe lecture is intended for Master students in computer science or mathematics, but might also be interesting for students from other disciplines. If in doubt, please simply attend the first lecture and talk to me in the end.

## Who, when, where

Lectures and tutorials: Monday, 12 c.t. - 14, and Wednesday, 16 c.t. - 18 (Hörsaal 1, Room F119, Sand 6).**First lecture is on October 15.**

## Course registration

If you wish to participate in this course, please register on ILIAS by October 22. We will provide further information about the course and the assignments on ILIAS.## Schedule and online material

Please check here for detailed schedule and course material.## Contents of the course

This course will provide an overview of the fundamental concepts and principles of statistical network analysis. We will cover the theoretical and algorithmic aspects of network modelling and analysis. On the mathematical side, this lecture will cover the following topics:- statistical models for networks
- theory of random graphs
- spectral graph theory
- random walks on networks

- network measures (such as degree distribution, motifs etc) and their uses
- spectral algorithms for embedding and learning on networks
- network dynamics
- network visualisation

## Prerequisites

You need to know the basics of probability theory and linear algebra, as taught in the mathematics for computer science lectures in your bachelor degree. If you cannot remember them so well, I strongly urge you to recap the material. Familiarity with machine learning may help in part of the course, but it is not essential.## Assessment criteria and exams

Bi-weekly assignments (theoretical assignments and programming assignments). You need to achieve at least 50% of all assignment points to be admitted to the final exam.Final written exam (Klausur): 11 February 2019, 12 c.t. - 14 (Hörsaal 1, Room F119, Sand 6)

Nachklausur: 10 April 2019, 10 c.t. - 12 (Hörsaal 1, Room F122, Sand 6)

## Literature

This lecture will cover a wide range of topics on networks, which cannot be found in a single book. We are going to use individual chapters of various books. Here is a list of few relevant books. We will refer to few research papers also, which will be mentioned during the course.- Chung, Lu: Complex graphs and networks.
- Easley, Kleinberg: Networks, crowds and markets.
- Chung: Lectures on spectral graph theory.