Predicting research collaboration based on translation model

[Objective] This paper proposes a modified translation model (TransTopic) to predict research cooperation, aiming to promote exchanges among researchers and maximize efficiency. [Methods] We used TransTopic to uniformly map the nodes and edges of the scientific research cooperation network to low-dimensional vectors. First, we used the LDA model to extract the topic distribution features of stem cells papers. Then, we turned topic features to edge vectors with the deep autoencoder and obtained node vectors based on the translation mechanism. Finally, we predicted the scientific cooperation through the semantic calculation between the vectors. [Results] TransTopic’s AUC (95.21%) and MeanRank (17.48) indicators for link prediction are better than those of the existing models, and its topic prediction accuracy rate reached 86.52%. [Limitations] The proposed method only considered a one-step translation path, and did not fully utilized information like author’s institution, research interests, and publication levels. [Conclusions] The proposed method based on translation model could effectively predict research cooperation in the field of stem cells.