  This work provides a comprehensive derivation of the parameter gradients for
GATv2 [4], a widely used implementation of Graph Attention Networks (GATs).
GATs have proven to be powerful frameworks for processing graph-structured data
and, hence, have been used in a range of applications. However, the achieved
performance by these attempts has been found to be inconsistent across
different datasets and the reasons for this remains an open research question.
As the gradient flow provides valuable insights into the training dynamics of
statistically learning models, this work obtains the gradients for the
trainable model parameters of GATv2. The gradient derivations supplement the
efforts of [2], where potential pitfalls of GATv2 are investigated.
