Graph Neural Networks (GNNs) have garnered considerable interest due to their exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the majority of GNN-based approaches have been examined using well-annotated benchmark datasets, leading to suboptimal performance in real-world graph learning scenarios. To bridge this gap, the present paper investigates the problem of graph transfer learning in the presence of label noise, which transfers knowledge from a noisy source graph to an unlabeled target graph. We introduce a novel technique termed Balance Alignment and Information-aware Examination (ALEX) to address this challenge. ALEX first employs singular value decomposition to generate different views with crucial structural semantics, which help provide robust node representations using graph contrastive learning. To mitigate both label shift and domain shift, we estimate a prior distribution to build subgraphs with balanced label distributions. Building on this foundation, an adversarial domain discriminator is incorporated for the implicit domain alignment of complex multi-modal distributions. Furthermore, we project node representations into a different space, optimizing the mutual information between the projected features and labels. Subsequently, the inconsistency of similarity structures is evaluated to identify noisy samples with potential overfitting. Comprehensive experiments on various benchmark datasets substantiate the outstanding superiority of the proposed ALEX in different settings.

翻译：图神经网络（GNN）因其在广泛的图机器学习任务中表现出的卓越性能而备受关注。然而，大多数基于GNN的方法均在标注完善的基准数据集上评估，导致其在真实图学习场景中性能欠佳。为填补这一空白，本文研究了标签噪声存在下的图迁移学习问题，即将知识从带噪声的源图迁移至未标注的目标图。我们提出了一种名为平衡对齐与信息感知检测（ALEX）的新技术来应对这一挑战。ALEX首先利用奇异值分解生成蕴含关键结构语义的不同视图，从而借助图对比学习获得鲁棒的节点表示。为缓解标签偏移与领域偏移，我们估计先验分布以构建具有均衡标签分布的子图。在此基础上，引入对抗领域判别器以实现复杂多模态分布的隐式领域对齐。此外，我们将节点表示投影至不同空间，优化投影特征与标签间的互信息。随后，通过评估相似性结构的不一致性来识别存在潜在过拟合的噪声样本。在多个基准数据集上的综合实验验证了所提出的ALEX在不同设置下的显著优越性。