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

@article{Morris2019WeisfeilerAL,
  title={Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks},
  author={Christopher Morris and Martin Ritzert and Matthias Fey and William L. Hamilton and Jan Eric Lenssen and Gaurav Rattan and Martin Grohe},
  journal={ArXiv},
  year={2019},
  volume={abs/1810.02244}
}
  • Christopher Morris, Martin Ritzert, +4 authors Martin Grohe
  • Published 2019
  • Mathematics, Computer Science
  • ArXiv
  • 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. [...] Key Method These higher-order structures play an essential role in the characterization of social networks and molecule graphs. Our experimental evaluation confirms our theoretical findings as well as confirms that higher-order information is useful in the task of graph classification and regression.Expand Abstract

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