Beyond Topology: Generative Node Importance Estimation via Structure-Guided Semantic Reasoning

摘要

Node Importance Estimation (NIE) in Knowledge Graphs (KGs) aims to quantify the significance of entities, serving as a pivotal instrument for deciphering the latent mechanisms of social dynamics. However, existing methods are often confined to supervised paradigms and rely heavily on topological aggregation, resulting in limited generalization capability. To address these challenges, we propose GenNIE, the first end-to-end generative reasoning framework for NIE. Specifically, GenNIE leverages Large Language Models (LLMs) integrated with topological information to generate precise importance scores for entities in KGs. Furthermore, GenNIE introduces a Global-Structural Graph Perception mechanism to empower the LLMs with holistic graph cognition. Extensive experiments demonstrate the performance superiority of GenNIE and its robust generalization across diverse domains. Our code is available at https://github.com/CoffeyF/GenNIE.git.