Probabilistic Models and Real World Networks
Shankar Bhamidi, University of North Carolina
Owing to the availability of data on a wide array of real world networks, there has been a concerted effort on researchers in a number of fields including math, statistical physics, computer science and statistics to develop probabilistic models to explain the evolution of various features in these real world systems. For applied probabilists, this has resulted in wonderful playground of interesting mathematical problems also resulting in connections in a number of fields. In this talk I will describe a number of such connections including simple probabilistic models in Network Tomography and Phylogenetics; Models for social networks such as Twitter or change point detection and continuous time branching processes; coagulation models in chemistry and continuum scaling limits of dynamic models which incorporate limited choice in their evolution.