基于<bold>XGBoost-SHAP</bold>框架的科研合作频次归因分析

doi:10.3772/j.issn.1000-0135.2026.03.002

情报学报

2026, Vol. 45

Issue (3): 332-349 DOI: 10.3772/j.issn.1000-0135.2026.03.002

情报理论与方法

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基于XGBoost-SHAP框架的科研合作频次归因分析

彭兆祺^1,2, 石斌^1,2, 杨杰^1,2, 邓三鸿^1,2

1.南京大学信息管理学院，南京 210023
2.数据工程与知识服务省高校重点实验室（南京大学），南京 210023

Attribution Analysis of Research Collaboration Frequency Based on XGBoost-SHAP Framework

Peng Zhaoqi^1,2, Shi Bin^1,2, Yang Alex Jie^1,2, Deng Sanhong^1,2

1.School of Information Management, Nanjing University, Nanjing 210023
2.Key Laboratory of Data Engineering and Knowledge Services in Provincial Universities (Nanjing University), Nanjing 210023

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摘要科研合作频次的背后蕴含着复杂的合作特征，在揭示科研人员合作关系的强度与模式方面具有关键作用。本研究选取生物医学领域数据集PKG 2.0（PubMed knowledge graph 2.0）中的13220951条真实合作关系作为样本，旨在揭示不同合作频次背后的关键驱动因素和内在规律。首先，以合作频次作为表征合作关系强度的代理变量，并根据其数据分布特征将频次划分为低频、中频和高频合作；其次，选择研究相似度、成果生产模式、学术资本和个体属性等维度的10类差异性变量，作为描述作者间合作关系的特征体系，并运用XGBoost（extreme gradient boosting）算法捕捉高维特征间的复杂关联；最后，通过SHAP（shapley additive explanations）解释框架对预测模型进行归因分析，以评估特征对不同合作频次的影响程度和交互机制。本研究发现，作者间研究主题相似度在合作频次分类中发挥主要作用，其次为论文数量差异、总被引用次数差异和均被引次数差异。研究主题高相似是维持高频合作的核心要素，低频合作则更关注总量积累而非个体论文影响力。研究主题相似度、论文数量差异与H指数差异对合作频次的作用模式分别为双峰对称型、阈值稳定型及边际递减型。此外，高频合作对知识结构或影响力构成等异质性因素具有显著的包容性，其背后往往形成了以互补为导向的有效分工机制。

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	彭兆祺
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	杨杰
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关键词 ：科研合作, 合作频次, XGBoost-SHAP, 算法归因

收稿日期: 2025-04-15

基金资助:国家社会科学基金一般项目“基于科学数据关联的科研合作网络研究”（24BTQ041）。

作者简介: 彭兆祺，1999年生，博士研究生，主要研究领域为科研合作、科学计量；石斌，1999年生，博士研究生，主要研究领域为多模态数据处理技术；杨杰，2000年生，博士研究生，主要研究领域为科学计量、复杂网络、创新测度；邓三鸿，通信作者，1975年生，博士，教授，博士生导师，主要研究领域为科学计量、领域知识图谱、信息分析与科学评价，E-mail：sanhong@nju.edu.cn；

引用本文:

彭兆祺, 石斌, 杨杰, 邓三鸿. 基于XGBoost-SHAP框架的科研合作频次归因分析[J]. 情报学报, 2026, 45(3): 332-349.
Peng Zhaoqi, Shi Bin, Yang Alex Jie, Deng Sanhong. Attribution Analysis of Research Collaboration Frequency Based on XGBoost-SHAP Framework. 情报学报, 2026, 45(3): 332-349.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2026.03.002 或 https://qbxb.istic.ac.cn/CN/Y2026/V45/I3/332

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