Exploring Co-author Networks in the Field of Generative Artificial Intelligence Research

As the development and application of generative artificial intelligence (AI) continue to increase across various fields, the need for collaborative research is growing. This study explores the collaborative research structure and identifies trends in cooperation within generative AI research, ultimately aiming to establish an infrastructure. To conduct the study, a co-author network analysis was performed based on generative AI-related papers collected using several keywords from the Korea Citation Index. Since 2021, research on generative AI has been actively conducted across various fields, including social sciences, engineering, and humanities, with more than 182 papers published in 2023 alone. The analysis results showed that researchers mainly preferred solo research, and collaboration between institutions was minimal. The co-author network's density was very low, indicating a lack of collaboration among generative AI researchers. Additionally, authors affiliated with the specific univeristy played a significant intermediary role within the network. The findings of this study emphasize the need for collaborative research to enhance the quality of generative AI research by exploring the co-author network through social network analysis and suggesting future directions for global co-author network analysis.