Graph neural networks (GNN) are complex Machine Learning models that solve various graph tasks such as node classification, graph classification or link prediction. Due to their complexity, they are treated as black boxes, and how they perform their prediction is difficult to understand. In recent years, explainers for Machine Learning models have been introduced, among them methods based on game theory. These methods try to explain the decision by computing the importance of the features by considering them as players of a cooperative game who cooperate in order to make the decision. A player’s impact on the decision is measured by his marginal contribution to a coalition of players. Different measures built on this principle exist, and they differ in the axioms they satisfy. In this article, we consider two such measures that we adapt to explain GNN.