3 papers
cs.LG2026
AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification
Xixun Lin, Zhiheng Zhou, Zhengyin Zhang +9
Graph classification is a core task in graph data mining with widespread real-world applications. Recent advances in graph neural networks (GNNs) have led to substantial performanc…
cs.LG2025
E2E-GRec: An End-to-End Joint Training Framework for Graph Neural Networks and Recommender Systems
Rui Xue, Shichao Zhu, Liang Qin +1
Graph Neural Networks (GNNs) have emerged as powerful tools for modeling graph-structured data and have been widely used in recommender systems, such as for capturing complex user-…
cs.CL2025
SPECTRA: Revealing the Full Spectrum of User Preferences via Distributional LLM Inference
Luyang Zhang, Jialu Wang, Shichao Zhu +4
Large Language Models (LLMs) are increasingly used to model user preferences, with the typical output as a directly-generated ranked item list per user. However, this generative pa…