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Despite growing interest in graph-based models for knowledge recombination prediction using academic knowledge graphs, existing approaches suffer from significant limitations: they fail to learn informative and robust knowledge entity representations by neglecting high-order information, inadequately account for real-world dynamics, and crucially, cannot provide readable rationales for their predictions. We address these challenges with H2GLM, which reformulates traditional graph learning as heterogeneous hypergraph learning to capture high-order information, incorporating a variational autoencoder (VAE) mechanism to enhance informativeness and robustness. Our approach then integrates large language models (LLMs) with the learned graph contextual information through a step-wise methodology, enabling evidence-supported decisions with clear, readable rationales. Experimental results highlight that H2GLM outperforms previous strong graph-based and LLM-based baselines by 4% to 8% in accuracy, 3% to 9% in AUC and 5% to 8% in F1 on extensive academic knowledge graphs containing over 1,000,000 nodes, with a small amount of training data. Visualizations and case studies further illustrate our method’s substantial utility over existing approaches in real-world scenarios. Further explainability and efficiency analyses underscore the practical value of our method
Chen et al. (Wed,) studied this question.