Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations Zhou, Hui Sun, Jinqing Zhao, Zhongying Yang, Yonghao Xie, Ailei Chiclana Parrilla, Francisco Relationship prediction Collaboration analysis Coauthor networks Deep learning It is challenging but important to predict the collaborations between different entities which in academia, for example, would enable finding evaluating trends of scientific research collaboration and the provision of decision support for policy formulation and incentive measures. In this paper, we propose an attention-based Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) model to predict the collaborations between different research affiliations, which takes both the influence of research articles and time (year) relationships into consideration. The experimental results show that the proposed model outperforms the competitive Support Vector Machine (SVM), CNN and LSTM methods. It significantly improves the prediction precision by a minimum of 3.23 percent points and up to 10.80 percent points when compared with the mentioned competitive methods, while in terms of the F1-score, the performance is improved by 13.48, 4.85 and 4.24 percent points, respectively. 2020-03-06T08:47:31Z 2020-03-06T08:47:31Z 2019-08-21 info:eu-repo/semantics/article Zhou, H., Sun, J., Zhao, Z., Yang, Y., Xie, A., & Chiclana, F. (2019). Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations. IEEE Access, 7, 118068-118076. http://hdl.handle.net/10481/60062 10.1109/ACCESS.2019.2936745 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España IEEE