Open-Set Cross-Network Node Classification via OOD-Guided Selective Alignment

Cross-network node classification transfers knowledge from a labeled source graph to an unlabeled target graph under distribution shifts. In open-set settings, aligning target nodes from unseen classes with known source classes can cause negative transfer. Existing open-set approaches primarily rely on prediction-related signals to guide adaptation, leaving distributional evidence insufficiently exploited for unknown identification. We propose OGSA, an out-of-distribution (OOD)-guided selective alignment framework that uses distributional deviation as complementary evidence for identifying unknown target nodes. Specifically, agreement between classifier predictions and prototype-initialized clustering provides initial target pseudo-labels. For nodes with conflicting predictions, structure-aware OOD scores provide additional evidence to identify potential unknown nodes. The refined assignments supervise target predictions and guide signed domain adversarial learning. Experiments on diverse graph benchmarks show that OGSA achieves competitive performance in both known class classification and unknown class recognition under open-set domain shifts.