Graph Neural Networks (GNNs) have gained considerable attention for their
potential in addressing challenges posed by complex graph-structured data in
diverse domains. However, accurately annotating graph data for training is
difficult due to the inherent complexity and interconnectedness of graphs. To
tackle this issue, we propose a novel graph representation learning method that
enables GNN models to effectively learn discriminative information even in the
presence of noisy labels within the context of Partially Labeled Learning
(PLL). PLL is a critical weakly supervised learning problem, where each
training instance is associated with a set of candidate labels, including both
the true label and additional noisy labels. Our approach leverages potential
cause extraction to obtain graph data that exhibit a higher likelihood of
possessing a causal relationship with the labels. By incorporating auxiliary
training based on the extracted graph data, our model can effectively filter
out the noise contained in the labels. We support the rationale behind our
approach with a series of theoretical analyses. Moreover, we conduct extensive
evaluations and ablation studies on multiple datasets, demonstrating the
superiority of our proposed method.

我们提出了一种新颖的图表示学习方法，使得图神经网络模型能够在部分标记学习的背景下有效地学习有区别的信息，通过融入辅助训练和潜在因果提取，我们的方法可以过滤掉标签中的噪声，理论分析和实验证明了我们方法的优越性。