3 papers
cs.AI2026
A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges
Chengcheng Sun, Yajie Song, Cheng Zhai +6
Graph Neural Networks (GNNs) have emerged as the leading paradigm for link prediction, enabling the inference of missing connections and the anticipation of potential future links.…
cs.LG2026
Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey
Chengcheng Sun, Jiayun Tian, Cheng Zhai +5
Graph Neural Networks (GNNs) have emerged as a powerful paradigm in Knowledge Graphs (KGs) due to their intrinsic ability to model graph-structured data. However, there remains a l…
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…