3 papers
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.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
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…