Abstract
An easy way for graph recognition algorithms is to use a two-step process: first, compute a characteristic feature as if the graph belongs to that class; second, check whether the computed feature really defines the input graph. Although in some cases the two steps can be merged, separating them may yield new and much more easily understood algorithms. In this paper we apply that paradigm to the cograph and distance hereditary graph recognition problems.