用大模型生成节点特征，提升量子计算语义网络的未来链接预测效果。
Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features
- 利用大模型自动构建节点特征，减少人工设计成本。
- 在量子计算语义网络上，显著优于传统嵌入方法。
- 适合对知识图谱和量子计算交叉研究感兴趣的读者。
量子计算在物理与计算机科学领域快速发展，具备解决复杂问题和加速计算过程的潜力。量子芯片的研发需理解多种实验条件间的关联。基于科学文献构建的语义网络，可表征概念间有意义的关系，在各领域用于识别知识空白与新颖概念组合。基于神经网络的链接预测方法在此类网络中已展现出潜力。本研究提出使用大模型（LLM）初始化节点特征，以增强图神经网络在链接预测任务中的节点表示能力。大模型能提供丰富描述，降低手工特征创建需求，减少成本。该方法在量子计算语义网络上，经多种链接预测模型验证，表现优于传统节点嵌入技术。
原文摘要 · Abstract (English)
Quantum computing is rapidly evolving in both physics and computer science, offering the potential to solve complex problems and accelerate computational processes. The development of quantum chips necessitates understanding the correlations among diverse experimental conditions. Semantic networks built on scientific literature, representing meaningful relationships between concepts, have been used across various domains to identify knowledge gaps and novel concept combinations. Neural network-based approaches have shown promise in link prediction within these networks. This study proposes initializing node features using LLMs to enhance node representations for link prediction tasks in graph neural networks. LLMs can provide rich descriptions, reducing the need for manual feature creation and lowering costs. Our method, evaluated using various link prediction models on a quantum computing semantic network, demonstrated efficacy compared to traditional node embedding techniques.
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