Bhack:

GitHub - danielegrattarola/spektral: Graph Neural Networks with Keras and… 


+1 Spektral.
There’s a TensorFlow talk on YouTube about GNNs by Petar Veličković (DeepMind) with a demo that uses Spektral:
Intro to graph neural networks (ML Tech Talks)

Note that since the library has changed since the video was created, there’re some code changes too (check the comments under the YouTube video). For example, in the beginning this could work in v1.0.x:
import spektral

dataset = spektral.datasets.citation.Citation(name='cora')
graph = dataset[0]
adj, features, labels = graph.a, graph.x, graph.y
train_mask, val_mask, test_mask = dataset.mask_tr, dataset.mask_va, dataset.mask_te
...

@JMG There’s another external library in TF Keras called kgcnn

GitHub: https://github.com/aimat-lab/gcnn_keras 5

Paper: Graph neural networks in TensorFlow-Keras with RaggedTensor representation (kgcnn) - ScienceDirect 1



"The focus of the graph package presented here is on a neat integration of graphs into the TensorFlow-Keras framework in the most straightforward way. Thereby, we hope to provide Keras graph layers which can be quickly rearranged, changed and extended to build custom graph models with little effort. This implementation is focused on the new TensorFlow’s RaggedTensor class which is most suited for flexible data structures such as graphs and natural language.”

Last year, there was a Google Graph Mining and Learning workshop at NeurIPS 2020: https://gm-neurips-2020.github.io/ - there were some insightful talks, check out the link.
If you’re trying to keep up with the latest GNN research and events, this GitHub may be useful:

  
      

      github.com
  

  
    

GitHub - naganandy/graph-based-deep-learning-literature: links to conference... 2


  links to conference publications in graph-based deep learning - GitHub - naganandy/graph-based-deep-learning-literature: links to conference publications in graph-based deep learning

  

  
    
    
  

  


P.S.
MNIST with GNNs using Keras and Spektral:

  
      

      kaggle.com
  

  
    

MNIST Graph Deep Learning 1

  Explore and run machine learning code with Kaggle Notebooks | Using data from Digit Recognizer