Node Importance Estimation (NIE) is crucial for integrating external information into Large Language Models through Retriever-Augmented Generation. Traditional methods, focusing on static, single-graph characteristics, lack adaptability to new graphs and user-specific requirements. CADReN, our proposed method, addresses these limitations by introducing a Contextual Anchor (CA) mechanism. This approach enables the network to assess node importance relative to the CA, considering both structural and semantic features within Knowledge Graphs (KGs). Extensive experiments show that CADReN achieves better performance in cross-graph NIE task, with zero-shot prediction ability. CADReN is also proven to match the performance of previous models on single-graph NIE task. Additionally, we introduce and opensource two new datasets, RIC200 and WK1K, specifically designed for cross-graph NIE research, providing a valuable resource for future developments in this domain.

翻译：节点重要性估计（NIE）对于通过检索增强生成将外部信息集成到大语言模型中至关重要。传统方法聚焦于静态单图特征，缺乏对新图及用户特定需求的适应性。我们提出的方法CADReN通过引入上下文锚定（CA）机制解决了上述局限。该机制使网络能够基于CA评估节点重要性，同时考虑知识图谱（KG）中的结构特征与语义特征。大量实验表明，CADReN在跨图NIE任务中表现更优，具备零样本预测能力；同时其在单图NIE任务中性能与现有模型相当。此外，我们构建并开源了专为跨图NIE研究设计的两个新数据集RIC200和WK1K，为该领域的未来发展提供了宝贵资源。