| 张斌,马费成.科学知识网络中的链路预测研究述评[J].中国图书馆学报,2015,41(3):99~113 |
| 科学知识网络中的链路预测研究述评 |
| A Review on Link Prediction of Scientific Knowledge Network |
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| DOI：10.13530/j.cnki.jlis.150016 |
| 中文关键词: 知识网络 链路预测 同质网络 异质网络 合作网络 引证网络 二分网络 |
| 英文关键词: Knowledge network Link prediction Homogeneous network Heterogeneous network Co-authorship network Citation network Bipartite network. |
| 基金项目:本文系国家自然科学基金面上项目“知识网络的形成机制及演化规律研究”(编号:71173249)和国家自然科学基金重点国际(地区)合作研究项目“大数据环境下的知识组织与服务创新研究”(编号:71420107026)的研究成果之一 |
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| 摘要点击次数: 4353 |
| 全文下载次数: 2487 |
| 中文摘要: |
| 本文以“科学知识网络中的链路预测”为主要对象，对链路预测的类型、研究思路和方法等相关理论进行了回顾，将知识网络划分为同质网络和异质网络两种类型，从合作网络、引证网络和二分网络三个方面对同质网络的研究进行梳理，并介绍了一些异质网络中的链路预测方法。认为：针对这方面的研究近年来有成为图书情报学领域研究热点的趋势；已有研究多是描述各种链路预测指标在不同类型知识网络中的预测效果，未来应当利用链路预测量化和评价演化模型，识别和分析异常链路，以发现知识热点和创新趋势，将知识网络的研究提升到应用层次。图5。表2。参考文献68。 |
| 英文摘要: |
Currently, the research about link prediction of knowledge network is scattered in the fields of Statistical Physics, Computer Science, Complex Networks and Library and Information Science. From the perspective of Library and Information Science, this paper mainly reviews researches on link prediction of knowledge network and systematically analyzes previous researches.
This paper used “link prediction” as topic to retrieve the literatures in WoS, and got 300 records. Using CitNetExplorer to analyze the direct citation relationships among the 300 records, we obtain the highly cited interrelated literatures to review on current link prediction types and research ideas.
Link prediction can be classified into two types:the static and the dynamic, and the corresponding dataset partition methods are different. The former uses random sampling, while the latter needs to consider the temporal state. In the field of Library and Information Science, knowledge networks vary in size and scale. So the link prediction of such knowledge networks uses the similarity-based algorithms in order to reduce computing complexity, but also introduce certain semantic and attribute information to ensure the accuracy of prediction.
This paper divides the knowledge network into homogeneous and heterogeneous networks. For the link prediction of homogeneous network, this paper reviews on the research progress from the aspects of co-authorship network, citation network and bipartite network. The co-authorship network can be viewed as an undirected network, and is the easiest way to describe the real network system. This paper summarizes the predictors and steps in link prediction of co-authorship network and examines its prediction effect from author, institution, and country level. The citation networks can be viewed as a directed network, and is the first proposed knowledge network. Compared to the co-authorship network, the citation network not only has the structure information of basic data, but also involves the external information, such as authors, journals and content of articles. Therefore, the citation network not only can be predicted based on the local structure information, but also by the external information, or by the integration of structural and external information to perform machine learning to improve the accuracy of link prediction. The bipartite network is a special kind of homogeneous network, which can be used to observe structural characteristics and evolution process between subject and object. Using link prediction in bipartite network can solve some problems of recommendation systems. For the link prediction of heterogeneous network, this paper reviews on some link prediction methods, such as forecasting model based on meta-path.
This paper finds out that link prediction of knowledge network in Library and Information Science has becomes a hot issue in recent years. Most of current studies are empirical studies describing the prediction effect in different types of knowledge networks based on a variety of predictors in order to determine the application scope of link prediction. This paper proposes that in the future research, the link prediction should be used to quantify and evaluate the evolution models, to identify and analyze anomalous links, and to discover knowledge hot spots and innovation trends. Ten years of development shows that knowledge network can be used as an ideal carrier for link prediction, and link prediction is a powerful tool for analyzing knowledge network. On this basis, carrying out applied research from link prediction perspective based on the structure and evolution of knowledge network will be the future research direction of knowledge network in the field of Library and Information Science. 5 figs. 2 tabs. 68 refs. |
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