2 papers
cs.LG2025
Out-of-Distribution Detection in Heterogeneous Graphs via Energy Propagation
Tao Yin, Chen Zhao, Xiaoyan Liu +1
Graph neural networks (GNNs) are proven effective in extracting complex node and structural information from graph data. While current GNNs perform well in node classification task…
cs.LG2025
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
Xu Chu, Hanlin Xue, Bingce Wang +5
Dynamic graph augmentation is used to improve the performance of dynamic GNNs. Most methods assume temporal locality, meaning that recent edges are more influential than earlier ed…