Graph neural networks (GNNs) have emerged as effective approaches for graph analysis, especially in the scenario of semi-supervised learning. Despite its success, GNN often suffers from over-smoothing and over-fitting problems, which affects its performance on node classification tasks. We analyze that an alternative method, the label propagation algorithm (LPA), avoids the aforementioned problems thus it is a promising choice for graph semi-supervised learning. Nevertheless, the intrinsic limitations of LPA on feature exploitation and relation modeling make propagating labels become less effective. To overcome these limitations, we introduce a novel framework for graph semi-supervised learning termed as Cyclic Label Propagation (CycProp for abbreviation), which integrates GNNs into the process of label propagation in a cyclic and mutually reinforcing manner to exploit the advantages of both GNNs and LPA. In particular, our proposed CycProp updates the node embeddings learned by GNN module with the augmented information by label propagation, while fine-tunes the weighted graph of label propagation with the help of node embedding in turn. After the model converges, reliably predicted labels and informative node embeddings are obtained with the LPA and GNN modules respectively. Extensive experiments on various real-world datasets are conducted, and the experimental results empirically demonstrate that the proposed CycProp model can achieve relatively significant gains over the state-of-the-art methods.

图神经网络 (GNN) 已成为图分析的有效方法，尤其是在半监督学习的场景中。尽管取得了成功，但 GNN 经常存在过度平滑和过度拟合的问题，这会影响其在节点分类任务上的性能。我们分析了一种替代方法，即标签传播算法（LPA），避免了上述问题，因此它是图半监督学习的一个有前途的选择。然而，LPA 在特征开发和关系建模方面的内在局限性使得传播标签变得不那么有效。为了克服这些限制，我们介绍图半监督学习称为一种新颖的框架的Cyc LIC标签支柱agation（CycProp缩写），它以循环和相互加强的方式将 GNNs 集成到标签传播过程中，以利用 GNNs 和 LPA 的优势。特别是，我们提出的 CycProp 使用标签传播的增强信息更新了 GNN 模块学习的节点嵌入，同时在节点嵌入的帮助下依次微调标签传播的加权图。模型收敛后，分别使用 LPA 和 GNN 模块获得可靠预测的标签和信息节点嵌入。对各种真实世界的数据集进行了大量实验，实验结果凭经验表明，所提出的 CycProp 模型可以比最先进的方法取得相对显着的收益。