Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks

4 Oct 2018Christopher Morris • Martin Ritzert • Matthias Fey • William L. Hamilton • Jan Eric Lenssen • Gaurav Rattan • Martin Grohe

In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Up to now, GNNs have only been evaluated empirically---showing promising results. We show that GNNs have the same expressiveness as the $1$-WL in terms of distinguishing non-isomorphic (sub-)graphs.

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