$\mathcal{G}^2Pxy$: Generative Open-Set Node Classification on Graphs with Proxy Unknowns

• URL: http://arxiv.org/abs/2308.05463v1
• Date: Thu, 10 Aug 2023 09:42:20 GMT
• Title: $\mathcal{G}^2Pxy$: Generative Open-Set Node Classification on Graphs with Proxy Unknowns
• Authors: Qin Zhang, Zelin Shi, Xiaolin Zhang, Xiaojun Chen, Philippe Fournier-Viger, Shirui Pan
• Abstract summary: We propose a novel generative open-set node classification method, i.e. $mathcalG2Pxy$. It follows a stricter inductive learning setting where no information about unknown classes is available during training and validation. $mathcalG2Pxy$ achieves superior effectiveness for unknown class detection and known class classification.
• Score: 35.976426549671075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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