通过大语言模型（LLM）初始化节点特征增强量子计算语义网络中的未来链路预测
Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features
AI总结：
本研究针对量子计算语义网络的链路预测任务，提出用LLM初始化节点特征以优化图神经网络节点表征的方法，经多种模型验证，效果优于传统节点嵌入技术，可减少人工特征构建成本。
摘要：
量子计算在物理学与计算机科学领域均发展迅猛，有望解决复杂问题并加速计算流程。量子芯片的研发需要厘清各类实验条件之间的关联。基于科学文献构建的语义网络可呈现概念间的有意义关联，已在多领域被用于识别知识空白与新型概念组合。基于神经网络的方法在这类网络的链路预测任务中展现出应用潜力。本研究提出利用大语言模型（LLM）初始化节点特征，以优化图神经网络链路预测任务中的节点表征。LLM可提供丰富的描述信息，减少人工特征构建的需求并降低成本。我们在量子计算语义网络上使用多种链路预测模型对该方法进行了评估，结果表明其效果优于传统节点嵌入技术。
英文摘要：
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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