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cs.LG2026
Empowering LLMs for Structure-Based Drug Design via Exploration-Augmented Latent Inference
Xuanning Hu, Anchen Li, Qianli Xing +3
Large Language Models (LLMs) possess strong representation and reasoning capabilities, but their application to structure-based drug design (SBDD) is limited by insufficient unders…
cs.LG2025
When Noisy Labels Meet Class Imbalance on Graphs: A Graph Augmentation Method with LLM and Pseudo Label
Riting Xia, Rucong Wang, Yulin Liu +3
Class-imbalanced graph node classification is a practical yet underexplored research problem. Although recent studies have attempted to address this issue, they typically assume cl…
cs.LG2025
Incomplete Graph Learning: A Comprehensive Survey
Riting Xia, Huibo Liu, Anchen Li +4
Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods ar…