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Graph Rewriting for Graph Neural Networks

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Graph Transformation (ICGT 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13961))

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Abstract

Given graphs as input, Graph Neural Networks (GNNs) support the inference of nodes, edges, attributes, or graph properties. Graph Rewriting investigates the rule-based manipulation of graphs to model complex graph transformations. We propose that, therefore, (i) graph rewriting subsumes GNNs and could serve as formal model to study and compare them, and (ii) the representation of GNNs as graph rewrite systems can help to design and analyse GNNs, their architectures and algorithms. Hence we propose Graph Rewriting Neural Networks (GReNN) as both novel semantic foundation and engineering discipline for GNNs. We develop a case study reminiscent of a Message Passing Neural Network realised as a Groove graph rewriting model and explore its incremental operation in response to dynamic updates.

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Author information

Authors and Affiliations

  1. University of Leicester, Leicester, UK

    Adam Machowczyk & Reiko Heckel

Authors
  1. Adam Machowczyk
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  2. Reiko Heckel
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Corresponding authors

Correspondence to Adam Machowczyk or Reiko Heckel .

Editor information

Editors and Affiliations

  1. King's College London, London, UK

    Maribel Fernández

  2. Singapore Management University, Singapore, Singapore

    Christopher M. Poskitt

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Machowczyk, A., Heckel, R. (2023). Graph Rewriting for Graph Neural Networks. In: Fernández, M., Poskitt, C.M. (eds) Graph Transformation. ICGT 2023. Lecture Notes in Computer Science, vol 13961. Springer, Cham. https://doi.org/10.1007/978-3-031-36709-0_16

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  • DOI: https://doi.org/10.1007/978-3-031-36709-0_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36708-3

  • Online ISBN: 978-3-031-36709-0

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