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The μG Language for Programming Graph Neural Networks

arXiv 2407.09441 ↗First posted 12 Jul 2024cs.FL cs.AI cs.LGcited by 0

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Abstract (arXiv)

Graph neural networks form a class of deep learning architectures specifically designed to work with graph-structured data. As such, they share the inherent limitations and problems of deep learning, especially regarding the issues of explainability and trustworthiness. We propose μG, an original domain-specific language for the specification of graph neural networks that aims to overcome these issues. The language's syntax is introduced, and its meaning is rigorously defined by a denotational semantics. An equivalent characterization in the form of an operational semantics is also provided and, together with a type system, is used to prove the type soundness of μG. We show how μG programs can be represented in a more user-friendly graphical visualization, and provide examples of its generality by showing how it can be used to define some of the most popular graph neural network models, or to develop any custom graph processing application.

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References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
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  11. 2017Robust Physical-World Attacks on Deep Learning Modelscited by 70
  12. 2020Graph Neural Networks in TensorFlow and Keras with Spektralcited by 59
6 more
  1. 2021Neuro-Symbolic AI: An Emerging Class of AI Workloads and their Characterizationcited by 22
  2. 2021The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphscited by 19
  3. 2018Innateness, AlphaZero, and Artificial Intelligencecited by 10
  4. 2022Fundamental Limits in Formal Verification of Message-Passing Neural Networkscited by 9
  5. 2021StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAscited by 5
  6. 2017DeepDSL: A Compilation-based Domain-Specific Language for Deep Learningcited by 4

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