论文标题
通过深度监督来解决图形神经网络中的过度光滑
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision
论文作者
论文摘要
通过图形神经网络（GNN）学习有用的节点和图形表示是一项具有挑战性的任务。众所周知，深度GNN遭受了过度平滑的影响，随着层数的增加，节点表示几乎无法区分，并且在下游任务上的模型性能会大大降低。为了解决这个问题，我们提出了深入监督的GNN（DSGNN），即GNNS通过深入的监督增强了，在所有层次中都将所有所学的表示都用于培训。我们从经验上表明，DSGNN对过度平滑的弹性有弹性，并且可以在节点和图形属性预测问题上超过竞争性基准。
Learning useful node and graph representations with graph neural networks (GNNs) is a challenging task. It is known that deep GNNs suffer from over-smoothing where, as the number of layers increases, node representations become nearly indistinguishable and model performance on the downstream task degrades significantly. To address this problem, we propose deeply-supervised GNNs (DSGNNs), i.e., GNNs enhanced with deep supervision where representations learned at all layers are used for training. We show empirically that DSGNNs are resilient to over-smoothing and can outperform competitive benchmarks on node and graph property prediction problems.
