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Predicting Co-Author Relationship in Medical Co-Authorship Networks
Research collaborations are encouraged because a synergistic effect yielding good results often appears. However, creating and organizing a strong research group is a difficult task. One of the greatest concerns of an individual researcher is locating potential collaborators whose expertise compleme...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4081126/ https://www.ncbi.nlm.nih.gov/pubmed/24991920 http://dx.doi.org/10.1371/journal.pone.0101214 |
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author | Yu, Qi Long, Chao Lv, Yanhua Shao, Hongfang He, Peifeng Duan, Zhiguang |
author_facet | Yu, Qi Long, Chao Lv, Yanhua Shao, Hongfang He, Peifeng Duan, Zhiguang |
author_sort | Yu, Qi |
collection | PubMed |
description | Research collaborations are encouraged because a synergistic effect yielding good results often appears. However, creating and organizing a strong research group is a difficult task. One of the greatest concerns of an individual researcher is locating potential collaborators whose expertise complement his best. In this paper, we propose a method that makes link predictions in co-authorship networks, where topological features between authors such as Adamic/Adar, Common Neighbors, Jaccard's Coefficient, Preferential Attachment, Katz(β), and PropFlow may be good indicators of their future collaborations. Firstly, these topological features were systematically extracted from the network. Then, supervised models were used to learn the best weights associated with different topological features in deciding co-author relationships. Finally, we tested our models on the co-authorship networks in the research field of Coronary Artery Disease and obtained encouraging accuracy (the precision, recall, F1 score and AUC were, respectively, 0.696, 0.677, 0.671 and 0.742 for Logistic Regression, and respectively, 0.697, 0.678, 0.671 and 0.743 for SVM). This suggests that our models could be used to build and manage strong research groups. |
format | Online Article Text |
id | pubmed-4081126 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-40811262014-07-10 Predicting Co-Author Relationship in Medical Co-Authorship Networks Yu, Qi Long, Chao Lv, Yanhua Shao, Hongfang He, Peifeng Duan, Zhiguang PLoS One Research Article Research collaborations are encouraged because a synergistic effect yielding good results often appears. However, creating and organizing a strong research group is a difficult task. One of the greatest concerns of an individual researcher is locating potential collaborators whose expertise complement his best. In this paper, we propose a method that makes link predictions in co-authorship networks, where topological features between authors such as Adamic/Adar, Common Neighbors, Jaccard's Coefficient, Preferential Attachment, Katz(β), and PropFlow may be good indicators of their future collaborations. Firstly, these topological features were systematically extracted from the network. Then, supervised models were used to learn the best weights associated with different topological features in deciding co-author relationships. Finally, we tested our models on the co-authorship networks in the research field of Coronary Artery Disease and obtained encouraging accuracy (the precision, recall, F1 score and AUC were, respectively, 0.696, 0.677, 0.671 and 0.742 for Logistic Regression, and respectively, 0.697, 0.678, 0.671 and 0.743 for SVM). This suggests that our models could be used to build and manage strong research groups. Public Library of Science 2014-07-03 /pmc/articles/PMC4081126/ /pubmed/24991920 http://dx.doi.org/10.1371/journal.pone.0101214 Text en © 2014 Yu et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Yu, Qi Long, Chao Lv, Yanhua Shao, Hongfang He, Peifeng Duan, Zhiguang Predicting Co-Author Relationship in Medical Co-Authorship Networks |
title | Predicting Co-Author Relationship in Medical Co-Authorship Networks |
title_full | Predicting Co-Author Relationship in Medical Co-Authorship Networks |
title_fullStr | Predicting Co-Author Relationship in Medical Co-Authorship Networks |
title_full_unstemmed | Predicting Co-Author Relationship in Medical Co-Authorship Networks |
title_short | Predicting Co-Author Relationship in Medical Co-Authorship Networks |
title_sort | predicting co-author relationship in medical co-authorship networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4081126/ https://www.ncbi.nlm.nih.gov/pubmed/24991920 http://dx.doi.org/10.1371/journal.pone.0101214 |
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