Academic collaborations improve research efficiency and spur scientific innovation. However, scholarly big data has hindered scholars from finding suitable collaborators. Although some studies have involved the prediction problem of academic collaborations, they neglect the rich dynamic information of the heterogeneous academic network. In this paper, we propose a prediction model for academic collaborations, which considers both the dynamic structure and content information. We first formally define the dynamic academic network and the collaboration prediction problem. Then, a scholar representation model is designed by capturing both the dynamic structure and content features, together with the macro-impact of overall academic trends. Finally, we build the prediction model based on the representation result of scholars. Extensive experiments for predicting new collaborations are conducted on the DBLP dataset. The experimental results on the accuracy, F1, and AUC metrics demonstrate that our method outperforms the baseline methods and can predict academic collaborations efficiently.

学术合作可提高研究效率并促进科学创新。然而，学术大数据阻碍了学者寻找合适的合作者。一些研究虽然涉及学术合作的预测问题，但忽略了异构学术网络丰富的动态信息。在本文中，我们提出了一种学术合作的预测模型，它同时考虑了动态结构和内容信息。我们首先正式定义动态学术网络和协作预测问题。然后，通过捕捉动态结构和内容特征，以及整体学术趋势的宏观影响，设计学者表征模型。最后，我们根据学者的表征结果建立预测模型。在 DBLP 数据集上进行了大量用于预测新合作的实验。准确率、F1 和 AUC 指标的实验结果表明，我们的方法优于基线方法，并且可以有效地预测学术合作。