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Graph Neural Networks (GNNs) have received a lot of interest in the recenttimes. From the early spectral architectures that could only operate onundirected graphs per a transductive learning paradigm to the current state ofthe art spatial ones that can apply inductively to arbitrary graphs, GNNs haveseen significant contributions from the research community. In this paper, weutilize some theoretical tools to better visualize the operations performed bystate of the art spatial GNNs. We analyze the inner workings of thesearchitectures and introduce a simple concept, Node Masking, that allows them togeneralize and scale better. To empirically validate the concept, we performseveral experiments on some widely-used datasets for node classification inboth the transductive and inductive settings, hence laying down strongbenchmarks for future research.

Node Masking: Making Graph Neural Networks Generalize and Scale Better