Bibliographic Coupling and Technological Advance Through the Use Vosviewer Software
DOI:
https://doi.org/10.33448/rsd-v11i9.31650Keywords:
Bibliographic coupling; Content analysis; Web of science base; Clusters; Vosviewer software.Abstract
The study aimed to perform the content analysis of 30 articles found in the bibliographic coupling referring to technological advances in the international literature based on Web of Science in the year 2020, through the use of the Vosviewer software. It should be noted that this article is a continuation of an infometric published in Research, Society and Development, in 2021, making only a clipping here which was called “Bibliographic Coupling”. The article has characteristics of exploratory, documentary and qualitative research. The analysis and description of 30 items/articles found in 7 Clusters were carried out. The study highlights the importance of information for the progress of societies worldwide. It is noteworthy from the reading of the 30 articles found in this bibliographic coupling that the advancement of technology has helped to find better techniques for achieving success in a given situation, whether in the medical, hospital, business areas, among others. The greater the number of references in common, the greater the strength of connection/links between the two articles, evidencing the intensity of the coupling of these two articles. Through the analysis of each article, the intention was achieved to understand the meanings and senses of the messages, which went beyond a common reading, elucidating in such a rich way the social phenomenon studied. With the use of a VOSViewer software, an improvement in the process of disclosure and transparency of the information found in the articles was obtained. Thus, rapid adaptations were achieved, as well as the time of analysis of articles and open data control in effective reproduction.
References
Bardin, L. (1977). Análise de Conteúdo. Edições 70.
Chen, Y., Yang, C., Zhang, Yan & Li, Y. (2020). Deep conditional adaptation networks and label correlation transfer for unsupervised domain adaptation. Pattern Recognition. 98, 107072, https://doi.org/10.1016/j.patcog.2019.107072.
Chesbrough, H. (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School.
Castanha, R. G. & Grácio, M. C. C. (2020). Indicadores de Acoplamento Bibliográfico para a avaliação da proximidade teórico-metodológica em redes de genealogia acadêmica um estudo aplicado aos descendentes bolsistas PQ de Aldo Barreto. RDBCI: Rev. Dig. Bibliotec e Ci. Info. / RDBCI: Dig. J. of Lib. and Info. Sci. 18, e020039. 10.20396/rdbci.v18i00.8661393
Ding, H. B., Pan, Z. B., Cen, Q. B., Li, Y. C. & Chen, S. A. (2020). Multi-scale fully convolutional network for gland segmentation using three-class classification. Neurocomputing. 380, 150–161. https://doi.org/10.1016/j.neucom.2019.10.097
Fang, Y., Huang, X., Qin, L., Zhang, Y., Zhang, W., Cheng, R. & Lin, X. (2020). A survey of community search over big graphs. The VLDB Journal. 29, 353–392. https://doi.org/10.1007/s00778-019-00556-x
Fu, J. A, B., Liu, J. A., Li, Y. C., Bao, Y. C., Yan, W. C., Fang, Z. A, B., & Lu, H.A. (2020). Contextual deconvolution network for semantic segmentation. Pattern Recognition. 101, 107152. https://doi.org/10.1016/j.patcog.2019.107152
Gao, P. A. B., Yuan, R. A., Wang, F. A., Xiao, L. A. B., Fujita, H. C. D. & Zhang, Y. a. (2019). Siamese Attentional Keypoint Network for High Performance. Visual Tracking. arXiv:1904.10128v2 [cs.CV] 29 Dec.
Grácio, M C. C. (2016). Acoplamento bibliográfico e análise de cocitação: revisão teórico-conceitual. Encontros Bibli: Revista eletrônica de biblioteconomia e ciência da informação, 21(47), 82-99. Universidade Federal de Santa Catarina.
Han, X., Zhang, Y., Zhang, W. & Huang, T. (2020). An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction. Information 2020, 11, 79; 10.3390/info11020079.
Hao, D. & Strotmann, A. (2008). Evolution of Research Activities and Intellectual Influences in Information Science 1996–2005: Introducing Author Bibliographic-Coupling Analysis. Journal of the American Society for Information Science and Tecnhology, 59(13), 2070-2086.
Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25.
Kessler, M. M. (1965). Comparison of the results of bibliographic coupling and analytic subject indexing. American Documentation, 16(3), 223–233.
Kirschbaum, C. (2013). Decisões sobre pesquisas Quali e Quanti sob a perspectiva de mecanismos causais. Revista Brasileira de Ciências Sociais, 28(82), 179-193.
Lei, K. a, b., Zhang, B. a., LI, Y a., Yang, M. c. & Shen, Y. a. (2020). Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading. Expert Systems with Applications. 140, 112872. https://doi.org/10.1016/j.eswa.2019.112872 0957-4174/© 2019 Elsevier Ltd. All rights reserved.
Leite, R. & Rocha, G. de A. (2019). Desenho de Pesquisa, Inferência e Causalidade: Caminhos Entre a Abordagem Qualitativa e Quantitativa. Revista Eletrônica de Ciência Política. 10(1), 107-119. 10.5380/recp. v%vi%i.61004 https://revistas.ufpr.br/politica/ ISSN: 2236-451X.
Li, H., Liu, Y., Mamoulis, N. & Fellow, D. S. R. (2019). Translation-Based Sequential Recommendation for Complex Users on Sparse Data. Ieee Transactions on Knowledge and Data Engineering. v. 32, n. 8, August 2020. Digital Object Identifier no. 10.1109/TKDE2906180. https://www.ieee.org/publications/rights/index.html for more information.
Li, J. a., Zhou, G. a, b., Qiu, Y. a., Wang, Y. a, b., Zhang, Y. d. & Xie, S. a, c. (2020). Deep graph regularized non-negative matrix factorization for multi-view clustering. Neurocomputing. V. 390, p. 108–116https://doi.org/10.1016/j.neucom.2019.12.054.
Li, J. a., Zhou, G. a, b., Qiu, Y. a., Wang, Y. a, b., Zhang, Y. d. & Xie, S. a, c. (2020). Deep graph regularized non-negative matrix factorization for multi-view clustering. Neurocomputing. V. 390, p. 108–116. https://doi.org/10.1016/j.neucom.2019.12.054.
Li, Y., Zeng, Y., Liu, T., Jia, X. & Bin, H. G. (2019). Simultaneously learning affinity matrix and data representations for machine fault diagnosis. Neural Networks. v. 122, p. 395–406, 2020. https://doi.org/10.1016/j.neunet. 11.007.
Liao, W. a., Wang, Y. a., Yin, Y. b., Zhang, X. a., & Ma, P. a. (2020). Improved sequence generation model for multi-label classification via CNN and initialized fully connection. Neurocomputing. V. 382, p. 188–195. https://doi.org/10.1016/j.neucom.2019.11.074 0925-2312/© 2019 Elsevier B.V. All rights reserved.
Liu, Y., Gao, X., Gao, Q., Han, J. & Shao, L. (2019). d. Label-activating framework for zero-shot learning. Neural Networks v.121 p.1–9, 2020. https://doi.org/10.1016/j.neunet..08.023 0893-6080/© 2019 Elsevier Ltd. All rights reserved.
Liu, Y., Zhang, J., Han, D., Wu, P., Sun, Y. & Moon, Y. S. (2020). A multidimensional chaotic image encryption algorithm based on the region of interest. Multimedia Tools and Applications. v.79, p. 17669–17705. https://doi.org/10.1007/s11042-020-08645-8
Liu, Y., Tobey H. K., Zhonglei G. & Jiming Liu, F. (2020). Identifying Key Opinion Leaders in social media via Modality-Consistent Harmonized Discriminant Embedding. IEEE TRANSACTIONS ON CYBERNETICS, v. 50, n. 2, FEBRUARY. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Liu, Y., Han, J., Zhang, Q. & Shan, C. (2020). Deep Salient Object Detection with Contextual Information Guidance. IEEE TRANSACTIONS ON IMAGE PROCESSING, v. 29. Digital Object Identifier 10.1109/TIP.2019.2930906. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Malheiros, B. T. & Tomei, P. A. (2022). Organizational Culture In Brazil: Bibliometric Study On International Bases. Rev. Pretexto, V. (23), n.01, p.60-77. Jan/Mar.
Mattos, A.M. & Dias, E.W. (2010). Análise de Cocitação de Autores: questões metodológicas. IN: XI Encontro Nacional de Pesquisa em Ciência da Informação: Inovação e inclusão social: questões contemporâneas da informação, Rio de Janeiro, 2010. Anais...
Moreira, P. S. Da C.; Guimarães, A. J. R.; Tsunoda, D. F. (2020). Qual ferramenta bibliométrica escolher? Um estudo comparativo entre softwares. P2P & INOVAÇÃO, Rio de Janeiro, v. 6 n. 2, Ed. Especial, p.140-158.
Miguel Peixe, A. M; Pinto, J. S, de P. (2021). Infometria nas Bases Web of Science e Scopus: Governança Corporativa, Informação e Tecnologia da Informação; Precificação de Ações e Riscos de Mercado. Research, Society and Development, v. 10, n. 5, e56110515433. (CC BY 4.0) | ISSN 2525-3409 | DOI: http://dx.doi.org/10.33448/rsd-v10i5.15433
Navarro, P. & Díaz, C. (1994). Análisis de contenido. In: Delgado, J. M.; Gutiérrez, J.(org.). Métodos y técnicas cualitativas de investigación em ciências sociales.Madrid: Editorial Sintesis Psicologia, p. 177-224.
Nie, X. 1., Zhang, W. 1,2., Zhang, Y. 1. & Yu, D. 1,2. (2020). Method to Predict Bursty Hot Events on Twitter Based on User Relationship Network. IEEE ACCESS. Digital Object Identifier 10.1109/ACCESS.2020.2977424. VOLUME 8. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/VOLUME 8, 2020.
Sun, F. a., Zang, W. a., Gravina, R. b., Fortino, G. b. & Li, Y. E. a. (2020). Gait-based identification for elderly users in wearable healthcare systems. Information Fusion v.53, p. 134–144. https://doi.org/10.1016/j.inffus.2019.06.023.
Telechi, A. V. & Novelli, D. H. (2021). A Tale Of Two Cities: A Bibliometric Review Of The 30 Years Of Academic Research On Mercosur. Revista Conjuntura Global, v. 10, número especial. DOI: 10.5380/CG.V10I3.83443
Van Eck, N. J. & Waltman, L. (2018). VOSviewer Manual.
Wang, R. a., Ma, X. a., Jiang, C. a., YE, Y. a. & Zhang, Y. b. (2020). Heterogeneous information network-based music recommendation system in mobile networks. Computer Communications. V. 150, p. 429–437. https://doi.org/10.1016/j.comcom.2019.12.002.
Wu, C. 1,2,3., Zhang, Y. 1,3., Zhang, Y. 1,3., Zhang, W. 1,3., Wang, H. 1,3., Zhang, Y. 1,2,3 & SUN, X. 1,3. (2019). Motion Guided Siamese Trackers for Visual Tracking. Digital Object Identifier 10.1109/ACCESS.2017.DOI. 10.1109/ACCESS.2020.2964269, IEEE Access. Date of current version December 24.
Wu, G. a., Miao, Y. a., Zhang, Y. a. & Barnawi, A. b. (2020) Energy efficient for UAV-enabled mobile edge computing networks: Intelligent task prediction and offloading. Computer Communications. v.150, p 556–562. https://doi.org/10.1016/j.comcom.2019.11.037.
Wu, Z., He, L., Wang, Y., Goh, M. & Ming, X. (2020). Knowledge recommendation for product development using integrated rough set‑information entropy correction. Journal of Intelligent Manufacturing. v.31, p. 1559–1578. https://doi.org/10.1007/s10845-020-01534-9.
Yan, X. a,b., Liu, Y. a. & Jia, M. b. (2020). Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions. Knowledge-Based Systems. v. 193, p. 105484. https://doi.org/10.1016/j.knosys.2020.105484.
Yao, Q., Wang, R., Fan, X., Liu, J. & Li, Y. (2020). Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network. Information Fusion. V.53, p. 174–182. https://doi.org/10.1016/j.inffus.2019.06.024.
Yu, L. 1,2,3., Xu, G. 1,2., Wang, Y. 1,2., Zhang, Y. 1,2,3. & Li, F. 1,2. (2020). ADPE: Adaptive Dynamic Projected Embedding on Heterogeneous Information Networks. ADPE on HINs. v. 8, p. 38970-38984. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/.
Zhang, Y. a,b., Huo, K. b., Liu, Z. b., Zang, Y. a., ; Liu, Y. b., Li, X. b., Zhang, Q. c. & Wang, C. a. (2020). PGNet: A Part-based Generative Network for 3D object reconstruction. Knowledge-Based Systems. V.194 p.105574. https://doi.org/10.1016/j.knosys.2020.105574 0950-7051/© 2020 Published by Elsevier B.V.
Zhang, Y. a., Wang, Y., Ying Liu, X a., Mi, S. b. & Ling Zhang, M. a, b. (2020). Large-scale multi-label classification using unknown streaming images. Pattern Recognition. v.99, p.107100, https://doi.org/10.1016/j.patcog.2019.107100 0031-3203/© 2019 Elsevier Ltd. All rights reserved.
Zhang, Z., Guangcan Liu, Y. Z., Tang, J., Fellow, S. Y. & Wang, M. (2020). Joint Label Prediction Based Semi-Supervised Adaptive Concept Factorization for Robust Data Representation. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v. 32, n.5, MAY. See ht_tps://www.ieee.org/publications/rights/index.html for more information.
Zhang, Z., Zhang, Y. 2. & Ren, Y. 1. (2020). Employing neighborhood reduction for alleviating sparsity and cold start problems in user‑based collaborative filtering. Information Retrieval Journal. v. 23, p. 449–472. https://doi.org/10.1007/s10791-020-09378-w.
Zhao, X. L. a., Hao Xu, W. a., Xiang Jiang, T. b., Wang, Y. c,d. & NG, MICHAEL K. e. (2020). Deep Plug-and-Play Prior for Low-Rank Tensor Completion. ArXiv:1905.04449v3 [cs.CV] 4 May.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Adriana Maria Miguel Peixe; Jose Simão de Paula Pinto
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
