We consider the problem of \textit{true} open-world semi-supervised node
classification, in which nodes in a graph either belong to known or new
classes, with the latter not present during training. Existing methods detect
and reject new classes but fail to distinguish between different new classes.
We adapt existing methods and show they do not solve the problem sufficiently.
We introduce a novel end-to-end approach for classification into known classes
and new classes based on class prototypes, which we call Prototypical
Open-World Learning for Node Classification (POWN). Our method combines graph
semi-supervised learning, self-supervised learning, and pseudo-labeling to
learn prototype representations of new classes in a zero-shot way. In contrast
to existing solutions from the vision domain, POWN does not require data
augmentation techniques for node classification. Experiments on benchmark
datasets demonstrate the effectiveness of POWN, where it outperforms baselines
by up to $20\%$ accuracy on the small and up to $30\%$ on the large datasets.
Source code is available at this https URL

我们提出了一种新的端到端方法，名为 Prototypical Open-World Learning for Node Classification（POWN），它结合了图形半监督学习、自监督学习和伪标签技术，以零样本方式学习新类别的原型表示，实验证明 POWN 在基准数据集上的效果优于基准模型，准确率提高了最多 20％（小数据集）和 30％（大数据集）.