Abstract Graph Neural Networks (GNNs) have become one of the indispensable tools to
learn from graph-structured data, and their usefulness has been shown in wide variety of
tasks. In recent years, there have been tremendous improvements in architecture design,
resulting in better performance on various prediction tasks. In general, these neural
architectures combine node feature aggregation and feature transformation using learnable
weight matrix in the same layer. This makes it challenging to analyze the importance of node …