DEAR: Decision Boundary-Aware Open-set Graph Domain Adaptation

Graph domain adaptation, a subfield of graph transfer learning, aims to transfer knowledge from a label-rich source domain to a related but label-scarce target domain. In the Open-Set Graph Domain Adaptation (OSGDA) setting, the target domain contains novel classes that are absent in the source domain, which introduces significant difficulty by interfering with the recognition of known classes. How to effectively transfer domain-invariant knowledge while distinguishing unknown classes remains a major challenge. Existing OSGDA approaches primarily focus on separating unknown classes in the target domain, often overlooking the limitations imposed by the robustness of representation learning in the source domain itself. To address this, we propose a novel decision boundary-aware method called DEAR. Specifically, we introduce a virtual class to compress the decision space of known classes, thereby reserving space in the decision boundary for unknown classes in the target domain. Then, within the target domain, we exploit the virtual class to mine intra-class relationships and accurately identify the decision boundary between known and unknown classes. Finally, we perform cross-domain contrastive learning based on feature and structure perturbations for the known-class nodes to further align transferable knowledge. Extensive experiments on multiple benchmark datasets demonstrate that achieves state-of-the-art performance.