Graph Neural Networks are an up-and-coming class of neural networks that operate on graphs and can therefore deal with connected, highly complex data. As explaining neural networks becomes more and more important, we investigate different ways to explain graph neural networks and contrast gradient-based explanations with the interpretability by design approach KEdge.We extend KEdge, to work with probability distributions different from HardKuma. Our goalis to test the performance of each method to judge which one works best under given circum-stances. For this, we extend the notion of fidelity from hard attribution weights to soft attributionweights and use the resulting metric to evaluate the explanations generated by KEdge, as wellas by the gradient-based techniques.We also compare the predictive performance of models that use KEdge with different distributions. Our experiments are run on the Cora, SightSeer, Pubmed, and MUTAG datasets. We find that KEdge outperforms the gradient based attribution techniques on graph classification problems and that it should be used with the HardNormal, HardKuma, or HardLaplace distributions, depending on if the top priority is model performance or attribution quality. To compare different metrics of judging attributions in the text domain, we visualize attribution weights generated by different models and find, that metrics whichcompare model attributions to human explanations lead to bad attribution weights.