A bootstrap percolation process on a graph with n vertices is an ‘infection’ process evolving in rounds. Let be fixed. Initially, there is a subset of infected vertices. In each subsequent round, every uninfected vertex that has at least r infected neighbors becomes infected as well and remains so forever.

We consider this process in the case where the underlying graph is an inhomogeneous random graph whose kernel is of rank one. Assuming that initially every vertex is infected independently with probability , we provide a law of large numbers for the size of the set of vertices that are infected by the end of the process. Moreover, we investigate the case , and we focus on the important case of inhomogeneous random graphs exhibiting a power-law degree distribution with exponent . The first two authors have shown in this setting the existence of a critical such that, with high probability, if , then the process does not evolve at all, whereas if , then the final set of infected vertices has size . In this work we determine the asymptotic fraction of vertices that will eventually be infected and show that it also satisfies a law of large numbers.

We propose a modification to the random destruction of graphs: given a finite network with a distinguished set of sources and targets, remove (cut) vertices at random, discarding components that do not contain a source node. We investigate the number of cuts required until all targets are removed, and the size of the remaining graph. This model interpolates between the random cutting model going back to Meir and Moon (J. Austral. Math. Soc. 11, 1970) and site percolation. We prove several general results, including that the size of the remaining graph is a tight family of random variables for compatible sequences of expander-type graphs, and determine limiting distributions for binary caterpillar trees and complete binary trees.