3 papers
cs.LG2025
Harnessing Feature Resonance under Arbitrary Target Alignment for Out-of-Distribution Node Detection
Shenzhi Yang, Junbo Zhao, Sharon Li +4
Detecting out-of-distribution (OOD) nodes in the graph-based machine-learning field is challenging, particularly when in-distribution (ID) node multi-category labels are unavailabl…
cs.LG2025
Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs
Shenzhi Yang, Bin Liang, An Liu +3
Given the critical role of graphs in real-world applications and their high-security requirements, improving the ability of graph neural networks (GNNs) to detect out-of-distributi…
cs.LG2025
NodeReg: Mitigating the Imbalance and Distribution Shift Effects in Semi-Supervised Node Classification via Norm Consistency
Shenzhi Yang, Jun Xia, Jingbo Zhou +2
Aggregating information from neighboring nodes benefits graph neural networks (GNNs) in semi-supervised node classification tasks. Nevertheless, this mechanism also renders nodes s…