Linearized Graph Neural Networks (GNNs) have attracted great attention in recent years for graph representation learning. Compared with nonlinear Graph Neural Network (GNN) models, linearized GNNs are much more time-efficient and can achieve comparable performances on typical downstream tasks such as node classification. Although some linearized GNN variants are purposely crafted to mitigate ``over-smoothing", empirical studies demonstrate that they still somehow suffer from this issue. In this paper, we instead relate over-smoothing with the vanishing gradient phenomenon and craft a gradient-free training framework to achieve more efficient and effective linearized GNNs which can significantly overcome over-smoothing and enhance the generalization of the model. The experimental results demonstrate that our methods achieve better and more stable performances on node classification tasks with varying depths and cost much less training time.

翻译：线性化图神经网络（GNNs）近年来在图表示学习领域引起了广泛关注。与非线性图神经网络模型相比，线性化GNNs在时间效率上显著提升，并且在节点分类等典型下游任务上能够达到相当的性能。尽管部分线性化GNN变体被特意设计以缓解“过平滑”问题，但实证研究表明它们仍在一定程度上受此困扰。本文从另一种视角出发，将过平滑与梯度消失现象相联系，提出了一种无梯度训练框架，以实现更高效、更有效的线性化GNNs，该框架能显著克服过平滑问题并增强模型的泛化能力。实验结果表明，我们的方法在不同深度下的节点分类任务中取得了更好且更稳定的性能，同时训练时间成本大幅降低。