LLM4Impact：整合异构信息进行科学影响力预测
LLM4Impact: Integrating Heterogeneous Information for Scientific Impact Prediction
- KE:SAI
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出LLM4Impact方法，整合语义、图、LLM和时间表示，通过门控机制和校准模块预测论文影响力，在200万论文基准上优于基线，RMSE降低10.13%。
AI中文摘要：
预测新发表论文的未来影响力具有挑战性，因为必须从发表时可获得的异构证据中推断。现有方法通常依赖单一信息源，或在不考虑其不同预测作用的情况下组合多个信息源。在本文中，我们提出了LLM4Impact，一种证据感知的科学影响力预测方法，它学习表示、整合和校准异构信息。LLM4Impact结合了语义、图、LLM和时间表示，并通过连续前缀令牌将图信息注入冻结的LLM。上下文感知的门控机制自适应地加权不同证据，而单独的校准模块则考虑引用规模的领域和时间变化。我们进一步构建了一个包含200万篇论文的大规模基准数据集，具有泄漏安全的时点异构自我中心图、时间划分以及年度和月度引用目标。实验表明，LLM4Impact在分布内测试集上持续优于强语义、图和基于LLM的基线，年度RMSE降低10.13%，在域外分布下降低6.87%。我们的结果表明，此类证据的价值取决于上下文：不同的论文受益于不同的信息源，而领域和发表时间影响证据如何转化为引用。这一发现促使自适应证据选择和上下文条件校准，而非仅仅更丰富的表示。我们将在发表后发布我们的代码、基准和交互式网络演示。
英文摘要：
Predicting the future impact of a newly published paper is challenging because it must be inferred from heterogeneous evidence available at publication time. Existing approaches often rely on a single source of information or combine multiple sources without accounting for their different predictive roles. In this paper, we present LLM4Impact, an evidence-aware method for scientific impact prediction that learns to represent, integrate, and calibrate heterogeneous information. LLM4Impact combines semantic, graph, LLM, and temporal representations, and injects graph information into a frozen LLM through continuous prefix tokens. A context aware gating mechanism adaptively weights different evidence, while a separate calibration module accounts for domain and temporal variation in citation scales. We further construct a large-scale benchmark dataset with 2 million papers, leakage-safe point-in-time heterogeneous ego graphs, temporal splits, and both year-level and month-level citation targets. Experiments show that LLM4Impact consistently outperforms strong semantic, graph, and LLM based baselines, with a 10.13% reduction in year RMSE on the in distribution test set and a 6.87% reduction under out-of-domain distribution. Our results reveal that the value of such evidence is context dependent: different papers benefit from different sources, while domain and publication time affect how evidence translates into citations. This finding motivates adaptive evidence selection and context-conditioned calibration rather than simply richer representations. We will release our code, benchmark, and an interactive web demonstration upon publication.
