mlsanityData to 14 Oct 2026

Implementing graph neural networks with TensorFlow-Keras

arXiv 2103.04318 ↗First posted 7 Mar 2021cs.LG cs.AIcited by 3

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Abstract (arXiv)

Graph neural networks are a versatile machine learning architecture that received a lot of attention recently. In this technical report, we present an implementation of convolution and pooling layers for TensorFlow-Keras models, which allows a seamless and flexible integration into standard Keras layers to set up graph models in a functional way. This implies the usage of mini-batches as the first tensor dimension, which can be realized via the new RaggedTensor class of TensorFlow best suited for graphs. We developed the Keras Graph Convolutional Neural Network Python package kgcnn based on TensorFlow-Keras that provides a set of Keras layers for graph networks which focus on a transparent tensor structure passed between layers and an ease-of-use mindset.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 41 of its 76 references that are in the map. First posted on arXiv 7 Mar 2021.

Builds on

41 of 76 references.

References · 41

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29 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1.

3 papers.

  1. 2022Graph neural networks for materials science and chemistrycited by 182
  2. 2025DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculescited by 13
  3. 2023A Crystal-Specific Pre-Training Framework for Crystal Material Property Predictioncited by 3