1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2026
Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection
Li Sun, Lanxu Yang, Jiayu Tian +6
Detecting out-of-distribution (OOD) graphs is crucial for ensuring the safety and reliability of Graph Neural Networks. In unsupervised graph-level OOD detection, models are typica…
cs.LG2024★ 1 cited
MDS-GNN: A Mutual Dual-Stream Graph Neural Network on Graphs with Incomplete Features and Structure
Peng Yuan, Peng Tang
Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing and learning representations from graph-structured data. A crucial prerequisite for the outstanding perfor…