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Semi-supervised node classification via adaptive graph smoothing networks
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DOI：10.1016/j.patcog.2021.108492.png)
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Abstract
En 中文
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 weak-ening 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 ac-cording 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. (c) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Adaptive graph smoothing networks
Graph convolutional networks
Semi-supervised learning
Graph node classification