4 papers
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…
Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning
Yu Liu, Yanan Cao, Xixun Lin +3
Knowledge graph completion (KGC) aims to infer new knowledge and make predictions from knowledge graphs. Recently, large language models (LLMs) have exhibited remarkable reasoning…
Beyond Pairwise Interactions: Equivariant Hypergraph Diffusion for Crystal Structure Prediction
Yang Liu, Chuan Zhou, Shuai Zhang +5
Crystal Structure Prediction (CSP) remains a fundamental challenge with significant implications for materials discovery and the advancement of various scientific disciplines. Rece…
Decision-focused Graph Neural Networks for Combinatorial Optimization
Yang Liu, Chuan Zhou, Peng Zhang +3
In recent years, there has been notable interest in investigating combinatorial optimization (CO) problems by neural-based framework. An emerging strategy to tackle these challengi…