【目的】为促进科研人员之间的交流合作，提出一种融合异质网络与表示学习的科研合作预测方法。【方法】运用学者、机构、论文、期刊等信息构建异质科研合作网络，根据网络中包含的学者之间不同的共现关系，将该异质网络划分为三种同质共现网络，再进一步利用Node2Vec和Doc2Vec算法分别学习学者的网络结构特征向量和内容属性特征向量，并进行融合。最后通过计算学者向量之间的余弦相似度进行合作预测。【结果】采用Web of Science数据库中人工智能领域的论文数据进行对比实验，本文所提预测方法的AUC值和F1值分别达到0.987 9和0.942 4，优于基线方法。【局限】对学者内容特征的表示没有考虑到学者的研究主题。【结论】本文方法考虑了学者的结构和内容属性，并结合异质网络，融合了机构、论文、期刊等多方面信息，能够得到更好的合作预测效果。
[Objective] This paper proposes a prediction method based on heterogeneous networks and representation learning. It tries to promote exchanges and cooperation among scientific researchers. [Methods] First, we constructed a heterogeneous scientific research cooperation network with information on scholars, institutions, papers, and journals. According to the different co-occurrence relationships among scholars included in the network, we divided the heterogeneous network into three types of homogenous co-occurrence networks. Then, we used Node2Vec and Doc2Vec to learn the network structure and content attribute features of scholars, respectively. Finally, we merged them to calculate the cosine similarity between scholars. [Results] We examined the new method with datasets in artificial intelligence from WOS. The proposed method’s predicted AUC and F1 values reached 0.987 9 and 0.942 4, respectively, outperforming the baseline methods. [Limitations] The representation of scholar content characteristics does not consider the scholar’s research topics. [Conclusions] The proposed model includes the scholar’s structure and content attributes. It also combines heterogeneous networks and integrates various information, including institutions, papers, and journals. The new method can predict scientific cooperation more effectively.
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