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dc.contributor.authorZhou, Hui
dc.contributor.authorSun, Jinqing
dc.contributor.authorZhao, Zhongying
dc.contributor.authorYang, Yonghao
dc.contributor.authorXie, Ailei
dc.contributor.authorChiclana Parrilla, Francisco 
dc.date.accessioned2020-03-06T08:47:31Z
dc.date.available2020-03-06T08:47:31Z
dc.date.issued2019-08-21
dc.identifier.citationZhou, 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.es_ES
dc.identifier.urihttp://hdl.handle.net/10481/60062
dc.description.abstractIt 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.es_ES
dc.description.sponsorshipThis work was supported in part by the Humanities and Social Science Research Project of the Ministry of Education in China under Grant 17YJCZH262 and Grant 18YJAZH136, in part by the National Natural Science Foundation of China under Grant 61303167, Grant 61702306, Grant 61433012, Grant U1435215, and Grant 71772107, in part by the Natural Science Foundation of Shandong Province under Grant ZR2018BF013 and Grant ZR2017BF015, in part by the Innovative Research Foundation of Qingdao under Grant 18-2-2-41-jch, in part by the Key Project of Industrial Transformation and Upgrading in China under Grant TC170A5SW, and in part by the Scientific Research Foundation of SDUST for Innovative Team under Grant 2015TDJH102.es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectRelationship predictiones_ES
dc.subjectCollaboration analysises_ES
dc.subjectCoauthor networkses_ES
dc.subjectDeep learninges_ES
dc.titleAttention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliationses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1109/ACCESS.2019.2936745


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Atribución 3.0 España
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