Analyzing Interdisciplinary Research Using Co-Authorship Networks
With the advancement of scientific collaboration in the 20th century, researchers started collaborating in many research areas. Researchers and scientists no longer remain solitary individuals; instead, they collaborate to advance fundamental understandings of research topics. Various bibliometric m...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/2524491 |
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| author | Mati Ullah Abdul Shahid Irfan ud Din Muhammad Roman Muhammad Assam Muhammad Fayaz Yazeed Ghadi Hanan Aljuaid |
| author_facet | Mati Ullah Abdul Shahid Irfan ud Din Muhammad Roman Muhammad Assam Muhammad Fayaz Yazeed Ghadi Hanan Aljuaid |
| author_sort | Mati Ullah |
| collection | DOAJ |
| description | With the advancement of scientific collaboration in the 20th century, researchers started collaborating in many research areas. Researchers and scientists no longer remain solitary individuals; instead, they collaborate to advance fundamental understandings of research topics. Various bibliometric methods are used to quantify the scientific collaboration among researchers and scientific communities. Among these different bibliometric methods, the co-authorship method is one of the most verifiable methods to quantify or analyze scientific collaboration. In this research, the initial study has been conducted to analyze interdisciplinary research (IDR) activities in the computer science domain. The ACM has classified the computer science fields. We selected the Journal of Universal Computer Science (J.UCS) for experimentation purposes. The J.UCS is the first Journal of Computer Science that addresses a complete ACM topic. Using J.UCS data, the co-authorship network of the researcher up to the 2nd level was developed. Then the co-authorship network was analyzed to find interdisciplinary among scientific communities. Additionally, the results are also visualized to comprehend the interdisciplinary among the ACM categories. A whole working web-based system has been developed, and a forced directed graph technique has been implemented to understand IDR trends in ACM categories. Finally, the IDR values between the categories are computed to quantify the collaboration trends among the ACM categories. It was found that “Artificial Intelligence” and “Information Storage and Retrieval”, “Natural Language Processing and Information Storage and Retrieval”, and “Human-Computer Interface” and “Database Applications” were found the most overlapping areas by acquiring an IDR score of 0.879, 0.711, and 0.663, respectively. |
| format | Article |
| id | doaj-art-810664546f904ccba75381f97213936f |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-810664546f904ccba75381f97213936f2025-02-03T06:47:21ZengWileyComplexity1099-05262022-01-01202210.1155/2022/2524491Analyzing Interdisciplinary Research Using Co-Authorship NetworksMati Ullah0Abdul Shahid1Irfan ud Din2Muhammad Roman3Muhammad Assam4Muhammad Fayaz5Yazeed Ghadi6Hanan Aljuaid7Institute of ComputingInstitute of ComputingInstitute of ComputingInstitute of ComputingDepartment of Software EngineeringDepartment of Computer ScienceDepartment of Computer ScienceComputer Sciences DepartmentWith the advancement of scientific collaboration in the 20th century, researchers started collaborating in many research areas. Researchers and scientists no longer remain solitary individuals; instead, they collaborate to advance fundamental understandings of research topics. Various bibliometric methods are used to quantify the scientific collaboration among researchers and scientific communities. Among these different bibliometric methods, the co-authorship method is one of the most verifiable methods to quantify or analyze scientific collaboration. In this research, the initial study has been conducted to analyze interdisciplinary research (IDR) activities in the computer science domain. The ACM has classified the computer science fields. We selected the Journal of Universal Computer Science (J.UCS) for experimentation purposes. The J.UCS is the first Journal of Computer Science that addresses a complete ACM topic. Using J.UCS data, the co-authorship network of the researcher up to the 2nd level was developed. Then the co-authorship network was analyzed to find interdisciplinary among scientific communities. Additionally, the results are also visualized to comprehend the interdisciplinary among the ACM categories. A whole working web-based system has been developed, and a forced directed graph technique has been implemented to understand IDR trends in ACM categories. Finally, the IDR values between the categories are computed to quantify the collaboration trends among the ACM categories. It was found that “Artificial Intelligence” and “Information Storage and Retrieval”, “Natural Language Processing and Information Storage and Retrieval”, and “Human-Computer Interface” and “Database Applications” were found the most overlapping areas by acquiring an IDR score of 0.879, 0.711, and 0.663, respectively.http://dx.doi.org/10.1155/2022/2524491 |
| spellingShingle | Mati Ullah Abdul Shahid Irfan ud Din Muhammad Roman Muhammad Assam Muhammad Fayaz Yazeed Ghadi Hanan Aljuaid Analyzing Interdisciplinary Research Using Co-Authorship Networks Complexity |
| title | Analyzing Interdisciplinary Research Using Co-Authorship Networks |
| title_full | Analyzing Interdisciplinary Research Using Co-Authorship Networks |
| title_fullStr | Analyzing Interdisciplinary Research Using Co-Authorship Networks |
| title_full_unstemmed | Analyzing Interdisciplinary Research Using Co-Authorship Networks |
| title_short | Analyzing Interdisciplinary Research Using Co-Authorship Networks |
| title_sort | analyzing interdisciplinary research using co authorship networks |
| url | http://dx.doi.org/10.1155/2022/2524491 |
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