Semi-supervised node classification via adaptive graph smoothing networks

•A novel graph neural network is proposed, which learns to reduce inter-class edge weight and produces smooth predictions accordingly.•Theoretical discussions of possible outcomes of the modified graphs under our framework are presented.•Experiments and ablation studies were conducted to show the effectiveness of our model, comparing with state-of-the-art graph neural networks.