The high cost of data labeling often results in node label shortage in real applications. To improve node classification accuracy, graph-based semi-supervised learning leverages the ample unlabeled nodes to train together with the scarce available labeled nodes. However, most existing methods require the information of all nodes, including those to be predicted, during model training, which is not practical for dynamic graphs with newly added nodes. To address this issue, an adversarially regularized graph attention model is proposed to classify newly added nodes in a partially labeled graph. An attention-based aggregator is designed to generate the representation of a node by aggregating information from its neighboring nodes, thus naturally generalizing to previously unseen nodes. In addition, adversarial training is employed to improve the model's robustness and generalization ability by enforcing node representations to match a prior distribution. Experiments on real-world datasets demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art methods. The code is available at https://github.com/JiarenX/AGAIN.

翻译：数据标注的高成本常导致实际应用中节点标签稀缺。为提高节点分类精度，基于图的半监督学习利用大量未标注节点与少量可用标注节点共同训练。然而，现有方法在模型训练时需获取包括待预测节点在内的全部节点信息，这对包含新增节点的动态图不具实用性。为解决该问题，本文提出一种对抗正则化图注意力模型，用于对部分标注图中的新增节点进行分类。通过设计基于注意力的聚合器，从邻域节点聚合信息生成节点表示，从而自然泛化至先前未见节点。此外，采用对抗训练方法迫使节点表示匹配先验分布，以提升模型鲁棒性与泛化能力。在真实数据集上的实验表明，所提方法相较于现有最优方法具有有效性。代码已开源至 https://github.com/JiarenX/AGAIN。