Review for NeurIPS paper: A graph similarity for deep learning

Summary and Contributions: The paper proposes a novel graph neural network architecture which is motivated both by the Weisfeiler-Lehman isomorphism test and kernel theory. In more detail, the paper states that the natural representation of a neighborhood of a node is not a simple sum but a sum of features in the kernel space, because this yields a meaningful kernel/distance between sets. Accordingly, the paper suggests that the lth-layer representation of a node should not be the transformed sum of previous layer representations in the neighborhood, but rather the sum of transformed previous-layer representations. This scheme is evaluated in node classification, graph classification, graph regression, and graph generation tasks, yielding performance gains in all cases, with particularly impressive gains in graph kernel classification.