Random networks with nonlinear preferential attachment
Seminar Room 1, Newton Institute
We define a dynamic model of random networks, where new vertices are connected to old ones with a probability proportional to a nonlinear function of their degree. Our main interest is in the phase transitions occuring when we vary the attachment function and move from weak to strong preferential attachment.
Properties discussed include the degree distribution, existence of a hub and clustering. This is joint work with Steffen Dereich (Marburg).