One of the key tasks in graph learning is node classification. While Graph neural networks have been used for various applications, their adaptivity to reject option setting is not previously explored. In this paper, we propose NCwR, a novel approach to node classification in Graph Neural Networks (GNNs) with an integrated reject option, which allows the model to abstain from making predictions when uncertainty is high. We propose both cost-based and coverage-based methods for classification with abstention in node classification setting using GNNs. We perform experiments using our method on three standard citation network datasets Cora, Citeseer and Pubmed and compare with relevant baselines. We also model the Legal judgment prediction problem on ILDC dataset as a node classification problem where nodes represent legal cases and edges represent citations. We further interpret the model by analyzing the cases that the model abstains from predicting by visualizing which part of the input features influenced this decision.

翻译：图学习中的关键任务之一是节点分类。尽管图神经网络已被广泛应用于各种场景，但其在拒绝选项设置下的适应性此前尚未得到探索。本文提出NCwR——一种在图神经网络中集成拒绝选项的节点分类新方法，使模型在不确定性较高时能够主动放弃预测。我们基于图神经网络框架，提出了适用于节点分类场景的基于代价和基于覆盖率的含弃权分类方法。在Cora、Citeseer和Pubmed三个标准引文网络数据集上进行了实验验证，并与相关基线方法进行了对比。同时，我们将ILDC数据集上的法律判决预测问题建模为节点分类问题，其中节点代表法律案例，边代表引用关系。通过可视化分析影响模型弃权决策的输入特征部分，进一步对模型进行可解释性探究。