Hi, I have a Boost.Graph object that needs to model as closely as possible a DAG in order to be a dependency graph. As such it is an adjacency_list with a directedS policy. All works fine, except that it also needs to work as the inverse of a dependency graph: it needs to find the vertices for a given vertex V that are the 'dependents' (?) of V, that is to say that all the found vertices depend upon V, not that V depends upon them. So I assume that to search for the dependents of vertex V I need to get all the in-edges of V, and the in-edges of all those in-edges recursively? This is apparently feasible by using a bidirectionalS policy for the graph. But my questions are the following: 1) Is there some other better way to do this, such as another graph traversal algorithm? 2) What's the computational expense, in general, of using a bidirectionalS graph over a directedS graph in terms of speed and memory? 3) In the event that there is no better way and a bidirectionalS graph is much fatter and slower than a directedS graph does it make sense to construct the bidirectionalS graph only when needed? Since I only need this when its requested, not as a permanent feature of the application, I could copy the entire directedS graph to create some other graph for this purpose. But here the graph size can vary from a few hundred vertices to several tens of thousands of vertices, so if there is a way to avoid this its probably best. Thanks for any insight here, Elisha Berns e.berns@computer.org tel. (310) 556 - 8332 fax (310) 556 - 2839