How to utilize the pseudo labels has always been a research hotspot in machine learning. However, most methods use pseudo labels as supervised training, and lack of valid assessing for their accuracy. Moreover, applications of pseudo labels in graph neural networks (GNNs) oversee the difference between graph learning and other machine learning tasks such as message passing mechanism. Aiming to address the first issue, we found through a large number of experiments that the pseudo labels are more accurate if they are selected by not overlapping partial labels and defined as negative node pairs relations. Therefore, considering the extraction based on pseudo and partial labels, negative edges are constructed between two nodes by the negative pseudo partial labels extraction (NP$^2$E) module. With that, a signed graph are built containing highly accurate pseudo labels information from the original graph, which effectively assists GNN in learning at the message-passing level, provide one solution to the second issue. Empirical results about link prediction and node classification tasks on several benchmark datasets demonstrate the effectiveness of our method. State-of-the-art performance is achieved on the both tasks.

通过实验发现，如果通过不重叠的部分标签选择伪标签，并将其定义为负节点对关系，则伪标签的准确性更高。因此，通过伪标签和部分标签的提取构建负边，生成一个带有高准确性伪标签信息的有向图，有效地辅助了图神经网络在消息传递层的学习，提供了第二个问题的解决方案。在几个基准数据集上的链路预测和节点分类任务的实证结果表明了我们方法的有效性，并取得了最先进的性能。

NP$^2$L：负面伪偏标签提取图神经网络