大数据2025，Vol.11Issue(2)：91-106,16.DOI:10.11959/j.issn.2096-0271.2025025
语言模型增强的引文网络连边因子挖掘
Language model-enhanced edge factor mining in citation network
摘要
Abstract
GNN is adept at aggregating information from neighboring nodes in graph-structured data to learn node representations,showing immense potential in the field of citation network data mining.However,most existing GNN lack a deep exploration of the factors driving edge information,which limits a thorough understanding and interpretation of complex relationships between nodes.For instance,the citation relationships between different papers are often driven by a variety of research topics.Despite attempts to enrich node and edge feature representations by integrating LLM with their strong textual understanding capabilities,these approaches have still not effectively sloved the problem of uncovering the underlying drivers of edge information.In light of this,an innovative framework was proposed—language model-enhanced edge factor mining,aimed to enhance the differentiation of edge relationship modeling in various GNN through a plug-in approach,exploring its application value in citation network link prediction scenarios.Coarse-grained factor mining extracted explicit category-related edge factors from citation network graphs containing documents by designing structured information prompts for LLM.Fine-grained factor mining used the K-means clustering algorithm to capture more detailed semantic topic-level edge factors from graph textual data.To verify the effectiveness of the proposed framework,experiments were conducted on three public datasets.The results demonstrate a significant advantage of language model-enhanced edge factor mining framework in improving the performance of various GNN models.关键词
大语言模型/图神经网络/连边因子挖掘/链接预测/引文网络Key words
large language mode/graph neural network/edge factor mining/link prediction/citation network分类
计算机与自动化引用本文复制引用
王慜懋,赵洪科,吴李康,焦之贤,黄振亚..语言模型增强的引文网络连边因子挖掘[J].大数据,2025,11(2):91-106,16.基金项目
国家自然科学基金项目(No.72101176) The National Natural Science Foundation of China(No.72101176) （No.72101176）
