文献–作者二分网络中基于路径组合的合著关系预测研究  

Predicting Co-authorship with Combination of Paths in Paper-author Bipartite Networks

作　　者：张金柱[1] 王小梅[2] 韩涛[2] 

机构地区：[1]南京理工大学经济管理学院,南京210094 [2]中国科学院文献情报中心,北京100190

出　　处：《现代图书情报技术》2016年第10期42-49,共8页New Technology of Library and Information Service

基　　金：国家自然科学基金青年基金"基于被引科学知识突变的突破性创新动态识别及其形成机理研究"(项目编号:71503125);教育部人文社会科学研究青年基金"异构知识网络中主题突变动态识别研究"(项目编号:14YJC870025);中央高校基本科研业务专项资金"基于专利引用科学知识突变的突破性创新动态识别方法与形成机理研究"(项目编号:30915013101)的研究成果之一

摘　　要：【目的】降低文献–作者二分网络在投影为合著网络过程中的信息丢失影响,形成适应特定二分网络的合著关系预测指标和方法,提高预测准确率和结果可解释性。【方法】首先构建文献–作者二分网络及其投影合著网络;接着抽取二分网络中的二阶路径和三阶路径表示作者间的关联关系;最后利用逻辑回归方法学习不同路径对于合著关系预测的贡献,由此形成文献–作者二分网络中基于路径组合的合著关系预测指标。【结果】在图书情报领域的实验证实,文献–作者二分网络在投影为合著网络过程中存在较大的信息丢失,并以合著关系预测准确率变化进行定量计算;逻辑回归方法适合学习不同路径对于合著关系预测的贡献,由此形成的路径组合指标准确率远远高出其他指标,并且预测结果更易解释。【局限】其他的多阶路径尚未引入到该模型中,方法通用性还需在其他领域进行验证。【结论】合著关系预测应直接在文献–作者二分网络上进行,以降低投影过程中的信息丢失影响;文献–作者二分网络上的路径组合指标是合著关系预测的最优指标;该方法可扩展应用到其他类型的二分网络中,如专利–发明人二分网络。[Objective] This paper aims to predict co-authorship more effectively and reduce the information loss. [Methods] First, we constructed a paper-author bipartite network and its co-authorship counterpart in the field of library and information science. Second, we described the relationships among authors with the path-length of two and three from the bipartite network. Third, we used the logistic regression method to learn the influence of different factors. Finally, we predicted co-authorship in the paper-author bipartite network with various indictors. [Results] We found significant information loss in the change from the paper-author bipartite network to the co-authorship network. The logistic regression method was an appropriate way to learn the contributions of paths. The new indicators were more accurate and the predicted co-authorships could be interpreted more easily. [Limitations] We did not include the multiple paths methods to the present study and more research is needed to examine the proposed method in other areas. [Conclusions] Co-authorship prediction should be conducted in the paper-author bipartite network to reduce the information loss. The paths combination indicator in the paper-author bipartite network might be the most effective method to predict co-authorship, which could be applied to the patent-inventor bipartite network.

关 键 词：文献–作者二分网络 路径组合指标 图书情报 合著网络 合著关系预测 

分 类 号：G353.1[文化科学—情报学]