Frenetic steering in a nonequilibrium graph

Starting from a randomly-oriented complete graph, an algorithm constructs equally-sized basins of attraction for a selection of vertices representing patterns. For a given driving amplitude, a random walker preferentially follows the orientation, while the nondissipative activity is considered variable. A learning algorithm updates the time-symmetric factor in the transition rates, in order that the walker will quickly reach a pattern and remain there for a sufficiently long time, whenever starting from a vertex in its basin of attraction. No use is made of a potential or cost function where patterns would correspond to local minima, but the nonequilibrium features of the walk are essential for frenetic steering