Feedback-based Graph Convolutional Networks

Applying graph convolutional networks to efficient nodes classification is still a hot spot in the research area of artificial intelligence. In order to fully exploit the neuron information in the graph model, we propose a feedback-based graph neural network, where the graph network takes the information of the known labels and the output of the model as the targets to optimize the network. Then, a top-down manner is adopted to select neurons associated with the target and remove the ones with negative contribution weights through a gating mechanism. The experimental results on benchmark datasets show that our proposed networks remarkably outperform the comparison methods.