Multi-feature adaptive-fusion enhanced graph neural network for open-set node classification

Semi-supervised open-set node classification is vital in graph learning, aiming to classify seen class nodes while identifying unseen class ones. Existing methods face limitations: they use fixed weight or simple concatenation for multi-view fusion, ignoring node-specific heterogeneity and inter-view relationships, and rely on global thresholds for novelty detection, which overlooks class-wise distribution variations. To address these issues, we introduce a Multi-feature Adaptive-fusion Enhanced graph neural Network (MAEN) for open-set node classification. MAEN comprises two key components: (1) A view-aware adaptive fusion mechanism that dynamically integrates multi-view features using a mixture-of-experts-guided weight generation strategy, effectively capturing node-specific characteristics and nuanced inter-view dependencies. (2) A distribution-aware rejection strategy that constructs class-adaptive decision boundaries by modeling the probability density distributions of seen classes, ensuring precise identification of novel-class instances. Experiments show that MAEN outperforms baseline methods across benchmark datasets, achieving significant improvements in both closed-set and open-set tasks.