Poisson approximation for cycles in the generalised random graph

The generalised random graph contains n vertices with positive i. i. d. weights. The probability of adding an edge between two vertices is increasing in their weights. We require the weight distribution to have finite second moments and study the point process Cₙ on \3, 4, \, which counts how many cycles of the respective length are present in the graph. We establish convergence of Cₙ to a Poisson point process. Under the stronger assumption of the weights having finite fourth moments we provide the following results. When Cₙ is evaluated on a bounded set A, we provide a rate of convergence. If the graph is additionally subcritical, we extend this to unbounded sets A at the cost of a slower rate of convergence. From this we deduce the limiting distribution of the length of the shortest and the longest cycle when the graph is subcritical, including rates of convergence. All mentioned results also apply to the Chung-Lu model and the Norros-Reittu model.