Models and Algorithms for Complex Networks Winter 2006 |

Home Announcements Homework Reading List References Datasets and Code Interesting Links |
Administrative 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 Overview 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.
Course HomeworkIt 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
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 Reading material, references and handouts 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. |
Announcements 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. |