  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 https://github.com/Bobowner/POWN.
