做学术影响力预测或科研评价的团队，这篇论文直接对比了图模型和LLM两种路线，给出了冷启动场景下的最佳实践——有向引用图+文本嵌入组合最稳，GraphRAG未必比简单提示好。值得点开看具体实验设计和结论。
该研究提出将科学论文影响力预测建模为队列归一化的top-P%分类任务，并在统一框架下比较了基于图模型和基于大语言模型的方法。研究者构建了时序约束下的引用图和文本相似度图，生成Node2Vec表示，并与OpenAI文本嵌入结合。最佳监督配置结合有向引用图和文本嵌入，AUC达到约0.84-0.85。同时评估了基于GPT 5.5和5.4 Nano的GraphRAG设置，虽然LLM方法性能高（AUC达0.87），但检索上下文并未持续提升效果，仅用目标论文提示往往与GraphRAG提示表现相当。结论是结构和文本信号在监督预测中互补，但检索增强需谨慎评估。
From Node2Vec to GPT-based GraphRAG: scientific impact prediction across graph and language models
Identifying which newly published scientific papers are likely to become highly cited is important for prioritizing research attention, supporting editorial decisions, and guiding the allocation of scientific resources, particularly under cold-start conditions where little direct evidence is available at publication time. In this work, we formulate impact prediction as a cohort-normalized top-P% classification task and compare graph-based and LLM-based approaches under a unified framework. We construct citation and textual-similarity graphs under temporal constraints and generate Node2Vec representations, either alone or combined with OpenAI text embeddings. The best supervised configuration combines directed citation graphs with textual embeddings, reaching approximately 0.84-0.85 AUC. We also evaluate a GPT-based GraphRAG setup, using GPT 5.5 and 5.4 Nano, in which graph neighborhoods are used as contextual evidence for prediction. Although the LLM-based approach achieves high performance, retrieved context does not consistently improve results; target-only prompts often perform as well as or better than GraphRAG prompts achieving the 0.87 mark. These findings indicate that structural and textual signals are complementary for supervised prediction, while retrieval augmentation must be carefully evaluated against simpler LLM baselines.