Semi-supervised node classification via adaptive graph smoothing networks. (April 2022) - Zheng, Ruigang; Chen, Weifu; Feng, Guocan
Highlights: 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. Abstract: Inspections on current graph neural networks suggest us to reconsider the computational aspect of the final aggregation. We consider that such aggregations perform a prediction smoothing and impute their potential drawbacks to be the inter-class interference implied by the underlying graphs. We aim at weakening the inter-class connections so that aggregations focus more on intra-class relations and producing smooth predictions according to weakening results. We apply a metric learning module to learn new edge weights and combine entropy losses to ensure the correspondence between the predictions and the learnt distances so that the weights of inter-class edges are reduced and predictions are smoothed according to the modified graph. Experiments on four citation networks and a Wiki network show that in comparison with other state-of-the-art graph neural networks, the proposed algorithm can improve the classification accuracy.