A Review on Link Prediction of Scientific Know ledge Netw ork

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 CitN etE xplorer 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 accuracyof 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.