I am not sure I fully understand the question. Node embedding produces a representation of the graph structure (and its features), which is dictated by the downstream task. Directed graph can be handled by appropriate definition of the gradient/divergence operators (it’s less immediate when it comes to curvature). The iterations of a numerical solver can be thought of as several “layers” of a neural network through which you can backpropagate.