  Graph Neural Networks (GNNs) have received a lot of interest in the recent
times. From the early spectral architectures that could only operate on
undirected graphs per a transductive learning paradigm to the current state of
the art spatial ones that can apply inductively to arbitrary graphs, GNNs have
seen significant contributions from the research community. In this paper, we
utilize some theoretical tools to better visualize the operations performed by
state of the art spatial GNNs. We analyze the inner workings of these
architectures and introduce a simple concept, Node Masking, that allows them to
generalize and scale better. To empirically validate the concept, we perform
several experiments on some widely-used datasets for node classification in
both the transductive and inductive settings, hence laying down strong
benchmarks for future research.
