Introduction: Tracing the spread of ideas and the presence of influence is a question of special importance across a wide range of disciplines, ranging from intellectual history to cultural analytics, computational social science, and the science of science. Method: We collect a corpus of open source journal articles, generate Knowledge Graph representations using the Gemini LLM, and attempt to predict the existence of citations between sampled pairs of articles using previously published methods and a novel Graph Neural Network based embedding model. Results: We demonstrate that our knowledge graph embedding method is superior at distinguishing pairs of articles with and without citation. Once trained, it runs efficiently and can be fine-tuned on specific corpora to suit individual researcher needs. Conclusion(s): This experiment demonstrates that the relationships encoded in a knowledge graph, especially the types of concepts brought together by specific relations can encode information capable of revealing intellectual influence. This suggests that further work in analyzing document level knowledge graphs to understand latent structures could provide valuable insights.

本研究解决了追踪思想传播和影响存在的问题，利用知识图谱嵌入方法来区分引用与不引用的文章对。通过生成知识图谱表示和采用新型图神经网络嵌入模型，我们的研究表明，该方法在识别文献间的引用关系上优于传统方法，展示了其在解读知识影响和潜在结构分析方面的重要性。

利用知识图谱嵌入检测文本级知识影响力