Attention-based Deep Learning Model for Predicting Collaborations between Different Research Affiliations

Date

2019-08-21

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE

Type

Article

Peer reviewed

Yes

Abstract

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.

Description

open access article

Keywords

Citation

Zhao, Z., Sun, J., Zhang, Y. and Xie, A. and Chiclana, F. (2019) Attention-based Deep Learning Model for Predicting Collaborations between Different Research Affiliations. IEEE Access,

Rights

Research Institute

Institute of Artificial Intelligence (IAI)