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CADReN: Contextual Anchor-Driven Relational Network for Controllable Cross-Graphs Node Importance Estimation

arXiv 2402.05135 ↗First posted 6 Feb 2024cs.AI cs.CL cs.IRcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 6 Feb 2024.

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References · 17

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5 more
  1. 2022Structure-based drug design with geometric deep learningcited by 40
  2. 2020Personalized PageRank to a Target Node, Revisitedcited by 16
  3. 2021Structured Citation Trend Prediction Using Graph Neural Networkscited by 11
  4. 2020MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signalscited by 9
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