Link prediction plays an important role in scientific collaboration networks, and can favourably affect the organization of international scientific projects. In this paper, a meta-path computed prediction (MPCP) algorithm for link prediction among scientists and publications is presented. The MPCP algorithm is based on a heterogeneous information network model composed of authors and keywords in articles retrieved from the Web of Science database. Two kinds of meta-paths are defined: Author to Author to Author (A-A-A) and Author to Direction to Author (A-D-A). By calculating A-A-A and A-D-A using the heterogeneous information network model, the predictive strength of the links can be computed. The overlap of the meta-paths is also taken into account. By restoring links and calculating the number of restored links with different standard values, similar results are achieved for (quantum communication and link prediction). The number of restored links decreases as a special threshold value increases. The experimental studies show that, for any threshold value up to 1, at least 50% of links are restored. The results presented in this paper verify that the algorithm is a feasible means of predicting collaboration among scientists.

中文翻译：

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基于信息检索的科学协作网络链接预测

链接预测在科学合作网络中起着重要作用，并且可以有利地影响国际科学项目的组织。本文提出了一种用于科学家和出版物之间的链接预测的元路径计算预测（MPCP）算法。MPCP算法基于异构信息网络模型，该模型由从Web of Science数据库检索的文章中的作者和关键字组成。定义了两种元路径：作者到作者到作者（AAA）和作者到作者到指导（ADA）。通过使用异构信息网络模型计算AAA和ADA，可以计算链接的预测强度。还考虑了元路径的重叠。通过还原链接并计算具有不同标准值的已还原链接的数量，对于（量子通信和链路预测）也获得了类似的结果。还原的链接数量随着特殊阈值的增加而减少。实验研究表明，对于高达1的任何阈值，将至少恢复50％的链接。本文提出的结果证明该算法是预测科学家之间合作的一种可行方法。