G raph C onvolutional N etwork (GCN) has been commonly applied for semi-supervised learning tasks. However, the established GCN frequently only considers the given labels in the topology optimization, which may not deliver the best performance for semi-supervised learning tasks. In this paper, we propose a novel G raph C onvolutional N etwork with E stimated labels (E-GCN) for semi-supervised learning. The core design of E-GCN is to learn a suitable network topology for semi-supervised learning by linking both estimated labels and given labels in a centralized network framework. The major enhancement is that both given labels and estimated labels are utilized for the topology optimization in E-GCN, which assists the graph convolution implementation for unknown labels evaluation. Experimental results demonstrate that E-GCN is significantly better than s tate-o f-t he-a rt (SOTA) baselines without estimated labels.

ģ拍摄和Ç onvolutional Ñ etwork（GCN）已被普遍应用于半监督学习任务。但是，已建立的GCN经常仅在拓扑优化中考虑给定标签，这可能无法为半监督学习任务提供最佳性能。在本文中，我们提出了一种新颖的ģ拍摄和Ç onvolutional Ñ etwork与Ë刺激标签（E-GCN），用于半监督学习。E-GCN的核心设计是通过在集中式网络框架中链接估计的标签和给定的标签来学习适合半监督学习的网络拓扑。主要增强之处在于，给定标签和估计标签都用于E-GCN中的拓扑优化，这有助于图形卷积实现未知标签的评估。实验结果表明，E-GCN是显著优于小号tate- ö F-吨他-一个RT（SOTA）基线而不估计标签。