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...

Full description

Bibliographic Details
Main Authors: Hui Zhou, Jinqing Sun, Zhongying Zhao, Yonghao Yang, Ailei Xie, Francisco Chiclana
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