  Node classification is the task of predicting the labels of unlabeled nodes
in a graph. State-of-the-art methods based on graph neural networks achieve
excellent performance when all labels are available during training. But in
real-life, models are often applied on data with new classes, which can lead to
massive misclassification and thus significantly degrade performance. Hence,
developing open-set classification methods is crucial to determine if a given
sample belongs to a known class. Existing methods for open-set node
classification generally use transductive learning with part or all of the
features of real unseen class nodes to help with open-set classification. In
this paper, we propose a novel generative open-set node classification method,
i.e. $\mathcal{G}^2Pxy$, which follows a stricter inductive learning setting
where no information about unknown classes is available during training and
validation. Two kinds of proxy unknown nodes, inter-class unknown proxies and
external unknown proxies are generated via mixup to efficiently anticipate the
distribution of novel classes. Using the generated proxies, a closed-set
classifier can be transformed into an open-set one, by augmenting it with an
extra proxy classifier. Under the constraints of both cross entropy loss and
complement entropy loss, $\mathcal{G}^2Pxy$ achieves superior effectiveness for
unknown class detection and known class classification, which is validated by
experiments on benchmark graph datasets. Moreover, $\mathcal{G}^2Pxy$ does not
have specific requirement on the GNN architecture and shows good
generalizations.
