Pooling in Graph Convolutional Neural Networks

@article{Cheung2019PoolingIG,
  title={Pooling in Graph Convolutional Neural Networks},
  author={Mark Cheung and John Shi and Lavender Yao Jiang and Oren Wright and Jos{\'e} M. F. Moura},
  journal={2019 53rd Asilomar Conference on Signals, Systems, and Computers},
  year={2019},
  pages={462-466}
}
  • Mark CheungJohn Shi J. Moura
  • Published 1 November 2019
  • Computer Science
  • 2019 53rd Asilomar Conference on Signals, Systems, and Computers
Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems. We empirically evaluate several pooling methods for GCNNs, and combinations of those graph pooling methods with three different architectures: GCN, TAGCN, and GraphSAGE. We confirm that graph pooling, especially DiffPool, improves classification accuracy on popular graph classification datasets and find that, on average, TAGCN achieves comparable or better accuracy… 

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