We present an implementation of a scalable path deviation algorithm to find the k most kinetically relevant paths in a transition network, where each path is distinguished on the basis of having a distinct rate-limiting edge. The potential of the algorithm to identify distinct pathways that exist in separate regions of the configuration space is demonstrated for two benchmark systems with double-funnel energy landscapes, namely a model "three-hole" network embedded on a 2D potential energy surface and the cluster of 38 Lennard-Jones atoms (LJ38). The path cost profiles for the interbasin transitions of the two systems reflect the contrasting nature of the landscapes. There are multiple well-defined pathway ensembles for the three-hole system, whereas the transition in LJ38 effectively involves a single ensemble of pathways via disordered structures. A by-product of the algorithm is a set of edges that constitute a cut of the network, which is related to the discrete analog of a transition dividing surface. The algorithm ought to be useful for determining the existence, or otherwise, of competing mechanisms in large stochastic network models of dynamical processes and for assessing the kinetic relevance of distinguishable ensembles of pathways. This capability will provide insight into conformational transitions in biomolecules and other complex slow processes.