  Graph Networks are used to make decisions in potentially complex scenarios
but it is usually not obvious how or why they made them. In this work, we study
the explainability of Graph Network decisions using two main classes of
techniques, gradient-based and decomposition-based, on a toy dataset and a
chemistry task. Our study sets the ground for future development as well as
application to real-world problems.

Machine Learning;Artificial Intelligence;Machine Learning