  The explainability of deep networks is becoming a central issue in the deep
learning community. It is the same for learning on graphs, a data structure
present in many real world problems. In this paper, we propose a method that is
more optimal, lighter, consistent and better exploits the topology of the
evaluated graph than the state-of-the-art methods.
