This course discusses advanced topics from the field of network science. It builds on the topics that have been discussed in Web Science (706.716) course. Among other topics we will discuss the topics of network evolution and the connection between network structure and its function.
In recent years a new multidisciplinary research field called Network Science has emerged from various traditional fields such as computer science, physics, social science, or information theory. Network Science revolves around the investigation of properties of connections between individuals rather than on the investigation of individual properties. For example, the famous "Six Degrees of Separation" phenomenon from social sciences can be only explained by the existence of specific structural properties of social networks. Yet another example involves e.g. the success and growth of technologies such as the Internet or the Web. This fastest growth of any technology in the human history can be explained by simple dynamic properties (e.g. preferential attachment) of the network representations of the Internet and the Web.
In this course we will investigate and discuss some of such advanced questions in modern networks. We will mostly deal with information networks.
Course topics include:
In this course the students will:
At the end of this course the students will know how to:
This term the course is fully online. Each Friday 10:00-11:00 there will be a Q&A session in WebEx. The meeting information:
|Meeting number:||137 680 2842|
There will be 4 homework assignements in this lecture. Each assignment consists of 2 applied mathematics problems. The assignements will be made available in TeachCenter.
You (i) model a process taking place on a network, e.g. information spreading over Twitter, the flow of passengers in a traffic system, etc; (ii) detect communities in (a) large empirical network(s); (iii) empirically analyze (a) large empirical network(s); (iv) come up with your own idea. You decide on the methodology, e.g. by simulation, optimization, statistical inference, analytical, or a combined appraoch. For a desired network you perform experiments, obtain results and finally discuss the results.
Then, prepare 5 slides for the discussion:
For ideas, software and dataset repositories check these slides.
Projects and excercises will be discussed during lectures. We will try to find projects which are interesting and funny for both students and me ;-)
The total number of points that can be reached will be 80 (4x15 for homework + 20 for project).
There is a minimum number of points that you have to reach for both homework and project to pass the course:
The grading scheme is as follows: