Hybrid Low-Order and Higher-Order Graph Convolutional Networks

@article{Lei2020HybridLA,
  title={Hybrid Low-Order and Higher-Order Graph Convolutional Networks},
  author={Fangyuan Lei and Xun Liu and Qingyun Dai and Bingo Wing-Kuen Ling and Huimin Zhao and Yan Liu},
  journal={Computational Intelligence and Neuroscience},
  year={2020},
  volume={2020}
}
With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of parameters and high computational complexity. Therefore, we propose a hybrid lower-order and higher-order graph convolutional network (HLHG) learning model, which uses a weight sharing mechanism to reduce the number of network parameters. To reduce the… 

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