Graph Neural Networks (GNNs) have achieved notable success in learning from graph-structured data, owing to their ability to capture intricate dependencies and relationships between nodes. They excel in various applications, including semi-supervised node classification, link prediction, and graph generation. However, it is important to acknowledge that the majority of state-of-the-art GNN models are built upon the assumption of an in-distribution setting, which hinders their performance on real-world graphs with dynamic structures. In this article, we aim to assess the impact of training GNNs on localized subsets of the graph. Such restricted training data may lead to a model that performs well in the specific region it was trained on but fails to generalize and make accurate predictions for the entire graph. In the context of graph-based semi-supervised learning (SSL), resource constraints often lead to scenarios where the dataset is large, but only a portion of it can be labeled, affecting the model's performance. This limitation affects tasks like anomaly detection or spam detection when labeling processes are biased or influenced by human subjectivity. To tackle the challenges posed by localized training data, we approach the problem as an out-of-distribution (OOD) data issue by by aligning the distributions between the training data, which represents a small portion of labeled data, and the graph inference process that involves making predictions for the entire graph. We propose a regularization method to minimize distributional discrepancies between localized training data and graph inference, improving model performance on OOD data. Extensive tests on popular GNN models show significant performance improvement on three citation GNN benchmark datasets. The regularization approach effectively enhances model adaptation and generalization, overcoming challenges posed by OOD data.

翻译：图神经网络（GNN）凭借其捕捉节点间复杂依赖关系的能力，在基于图结构数据的学习中取得了显著成功，在半监督节点分类、链接预测和图生成等应用中表现优异。然而，大多数先进的GNN模型均建立在同分布假设之上，这一前提限制了其在动态结构真实图数据上的性能。本文旨在评估在图的局部子集上训练GNN所产生的影响。这种受限的训练数据可能导致模型在特定区域表现良好，但无法对整个图进行泛化预测。在基于图的半监督学习（SSL）场景中，资源约束常导致数据集规模庞大但仅能标注部分数据，从而影响模型性能。当标注过程存在偏差或受人类主观性影响时（如异常检测或垃圾邮件检测任务），这种局限性尤为突出。为解决局部化训练数据带来的挑战，我们将问题视为分布外（OOD）数据处理，通过对齐训练数据（小规模标注样本）与全图推理过程的数据分布，提出一种正则化方法最小化局部训练数据与全图推理之间的分布差异，从而提升模型在OOD数据上的表现。在主流GNN模型上的大量实验表明，该方法在三个经典GNN引文基准数据集上取得了显著的性能提升。所提出的正则化策略有效增强了模型的适应性与泛化能力，成功克服了OOD数据带来的挑战。