Course Code:582488
Instructor: Panayiotis Tsaparas
Tutor: Evimaria Terzi
Language: English
Time: January 16 - March 22,  Mon, Wed, 14 - 16
Lecture Room: Kumpula campus, Exactum bldg, Room B119
Office Hours:  Panayiotis, Wednesday 16:00 - 17:00, Room  A347
                        Evimaria, Monday 16:00- 17:00, Room A346
Tutorials: January 26 - March 30, Fri, 14 - 16, Room C220
Mailing list: macn2006-list (at) cs.helsinki.fi

In this course we will study models and algorithms for complex networks. The course will be very similar to the Information Networks course, taught last year.  We will study networks such as the Web, the internet, social networks and biological networks, and consider various generative models for these networks. The objective is to see the common structure and properties that underlie these networks, and study algorithms that make use of this structure for tasks such as ranking, information propagation, epidemic containment.

It is assumed that the students that take this course have a good grasp of mathematics. In particular you should have a good understanding of the basic concepts of

• Graph theory
• Asymptotic Complexity Notation
• Probabilities
• Linear Algebra

For the project (and some exercise sets) you will be may be required to do some programming.

The course homework consists of assignments, and a project. The assignments will be reaction papers, exercise sets, or a presentation (click here for more info on that). The exact number and type of assignments will be determined depending on the attendance and the material. The final grade will be determined 40% by the grade on the project and 60% by the grade on the assignments. More details on the homework will be posted shortly.

Evimaria will host tutorials on Thursdays, 14:00 - 16:00 pm

The goal of the course is to go through a collection of recent research papers on models and algorithms for complex networks. A (constantly changing) list of papers can be found here. The list is indicative of the topics that will be covered. Not all papers will be presented in class.

Teaching will be done using both slides and blackboard, depending on the topic. Slides and references will be made available on the Web page at the references page.

The latest announcements will be posted here. All announcements can be found at the announcements page

February 2: The matlab sample file for the first assignment can be downloaded from the homework page. It will also be available at the datasets and code page.