CADReN: Contextual Anchor-Driven Relational Network for Controllable
  Cross-Graphs Node Importance Estimation
Zijie Zhong Yunhui Zhang Ziyi Chang Zengchang Qin
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.