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 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 theACM 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.

Automated summary

Researchers and scientists began collaborating in various research areas in the 20th century, using the co-authorship method to quantify and analyze scientific collaboration, identifying interdisciplinary collaborations in computer science and providing important insights for research.