Fig. 7: Schematic of GNN-based feature extractor.

The feature extractor is comprised of a stack of graph convolution blocks, a memory pooling layer, and a residual connection between the original input node features and the output of the memory pooling layer. Some elements of the figure were rendered using functions from Matplotlib in Python, while the entire figure was created in PowerPoint.