Gradient Derivation for Learnable Parameters in Graph Attention Networks. (arXiv:2304.10939v1 [cs.LG])

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 …

applications arxiv attention data datasets derivation dynamics flow frameworks gradient graph implementation insights networks performance processing research structured data training work