Random graph models of complex networks

The last decade increasing computer power has made it possible to investigate and quantify large empirical networks such as social contact structures, communication networks, the Internet and the World Wide Web. Many of the networks have turned out to share similar properties, and this observation has led to a number of new random graph models intended to capture the observed properties in a better way than existing models. The course will provide an overview of the most studied random graph models for complex network. Implications of the models in applications (e.g. epidemic modeling) will also be described.

The course is suitable for PhD and master students in Mathematical Statistics. PhD and master students in Mathematics may also take the course provided they have basic knowledge in probability (roughly corresponding to the course Probability Theory II at SU).