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.