Semi-supervised node classification, as a fundamental problem in graph learning, leverages unlabeled nodes along with a small portion of labeled nodes for training. Existing methods rely heavily on high-quality labels, which, however, are expensive to obtain in real-world applications since certain noises are inevitably involved during the labeling process. It hence poses an unavoidable challenge for the learning algorithm to generalize well. In this paper, we propose a novel robust learning objective dubbed pairwise interactions (PI) for the model, such as Graph Neural Network (GNN) to combat noisy labels. Unlike classic robust training approaches that operate on the pointwise interactions between node and class label pairs, PI explicitly forces the embeddings for node pairs that hold a positive PI label to be close to each other, which can be applied to both labeled and unlabeled nodes. We design several instantiations for PI labels based on the graph structure and the node class labels, and further propose a new uncertainty-aware training technique to mitigate the negative effect of the sub-optimal PI labels. Extensive experiments on different datasets and GNN architectures demonstrate the effectiveness of PI, yielding a promising improvement over the state-of-the-art methods.
翻译:半监督节点分类是图表学习中的一个基本问题,因此,利用现有方法将未贴标签的节点与少量的标签节点一起用于培训。现有的方法严重依赖高质量的标签,然而,在真实世界的应用中,这些标签非常昂贵,因为在标签过程中,某些噪音不可避免地会涉及到某些噪音。因此,对学习算法的概括化来说,这是一个不可避免的挑战。在本文中,我们为模型提出了一个新颖的强有力的学习目标,称为双对互动(PI),例如图形神经网络(GNN),以对抗吵闹标签。不同于典型的稳健培训方法,在节点和类标签对配对之间的点互动上操作,PI明确要求为持有积极的 PI 标签的节点配对嵌入彼此接近的嵌入,这既可以适用于贴标签的节点,也可以适用于未贴标签的节点。我们根据图表结构和节点标签设计了一些PI 贴标签的即时解,并进一步提出新的不确定性培训技术,以缓解节点 PI 标签子性PI 和有希望的GI 结构的模型。