Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations
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...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8808879/ |
_version_ | 1819276311010476032 |
---|---|
author | Hui Zhou Jinqing Sun Zhongying Zhao Yonghao Yang Ailei Xie Francisco Chiclana |
author_facet | Hui Zhou Jinqing Sun Zhongying Zhao Yonghao Yang Ailei Xie Francisco Chiclana |
author_sort | Hui Zhou |
collection | DOAJ |
description | 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. |
first_indexed | 2024-12-23T23:38:12Z |
format | Article |
id | doaj.art-db0ce2a05ca648bd90a5bde7fd4c9f10 |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-12-23T23:38:12Z |
publishDate | 2019-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Access |
spelling | doaj.art-db0ce2a05ca648bd90a5bde7fd4c9f102022-12-21T17:25:49ZengIEEEIEEE Access2169-35362019-01-01711806811807610.1109/ACCESS.2019.29367458808879Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research AffiliationsHui Zhou0Jinqing Sun1Zhongying Zhao2https://orcid.org/0000-0002-5880-0225Yonghao Yang3Ailei Xie4Francisco Chiclana5Shandong Province Key Laboratory of Wisdom Mine Information Technology, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaShandong Province Key Laboratory of Wisdom Mine Information Technology, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaShandong Province Key Laboratory of Wisdom Mine Information Technology, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaShandong Province Key Laboratory of Wisdom Mine Information Technology, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaSchool of Education, Guangzhou University, Guangzhou, ChinaSchool of Computer Science and Informatics, Institute of Artificial Intelligence, De Montfort University, Leicester, U.K.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.https://ieeexplore.ieee.org/document/8808879/Relationship predictioncollaboration analysiscoauthor networksdeep learning |
spellingShingle | Hui Zhou Jinqing Sun Zhongying Zhao Yonghao Yang Ailei Xie Francisco Chiclana Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations IEEE Access Relationship prediction collaboration analysis coauthor networks deep learning |
title | Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations |
title_full | Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations |
title_fullStr | Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations |
title_full_unstemmed | Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations |
title_short | Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations |
title_sort | attention based deep learning model for predicting collaborations between different research affiliations |
topic | Relationship prediction collaboration analysis coauthor networks deep learning |
url | https://ieeexplore.ieee.org/document/8808879/ |
work_keys_str_mv | AT huizhou attentionbaseddeeplearningmodelforpredictingcollaborationsbetweendifferentresearchaffiliations AT jinqingsun attentionbaseddeeplearningmodelforpredictingcollaborationsbetweendifferentresearchaffiliations AT zhongyingzhao attentionbaseddeeplearningmodelforpredictingcollaborationsbetweendifferentresearchaffiliations AT yonghaoyang attentionbaseddeeplearningmodelforpredictingcollaborationsbetweendifferentresearchaffiliations AT aileixie attentionbaseddeeplearningmodelforpredictingcollaborationsbetweendifferentresearchaffiliations AT franciscochiclana attentionbaseddeeplearningmodelforpredictingcollaborationsbetweendifferentresearchaffiliations |