  The graph transformation approach is a recently proposed method for computing
mean first passage times, rates, and committor probabilities for kinetic
transition networks. Here we compare the performance to existing linear algebra
methods, focusing on large, sparse networks. We show that graph transformation
provides a much more robust framework, succeeding when numerical precision
issues cause the other methods to fail completely. These are precisely the
situations that correspond to rare event dynamics for which the graph
transformation was introduced.
