CADReN: Contextual Anchor-Driven Relational Network for Controllable Cross-Graphs Node Importance Estimation
This addresses the need for more flexible and user-specific node importance estimation in knowledge graphs for applications like Retriever-Augmented Generation, though it appears incremental as it builds on existing NIE methods.
The paper tackles the problem of Node Importance Estimation (NIE) for integrating external information into Large Language Models, proposing CADReN to improve adaptability to new graphs and user-specific requirements, achieving better performance in cross-graph NIE with zero-shot ability and matching previous models on single-graph tasks.
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.