A Holistic Approach to Studying Interdisciplinary Collaboration: Computer Science

Research collaboration networks play a crucial role in shaping the evolution of scientific fields. In computer science, interdisciplinary research has created a significant impact. It has contributed to merging knowledge from one discipline to another, generating new innovations and enriching all involved. For example, Hadoop was developed to enable large-scale distributed data processing, inspired by Google's MapReduce and GFS. Similarly, modern VR headsets emerged from advancements in graphics, system design, and motion tracking technologies. In the past few years, AI-driven research has influenced various domains and brought revolutionary changes to computer science. Understanding the growth of these domains requires a different aspect of analysis rather than focusing on a single aspect. To examine interdisciplinary collaboration, we investigate three key aspects: (i) Topic Modeling – identifying trending subdisciplinary topics and their evolution; (ii) Cross-Disciplinary Authors – detecting bridging researchers who facilitate knowledge exchange; and (iii) Industry Collaboration – analyzing institutional partnerships and their role in shaping research trajectories. This study compares four major computer science disciplines—Human-Computer Interaction, Computer Vision, Software Engineering, and Data Mining—using DBLP and ORCID data. Through a temporal analysis of topics, we identify emerging and declining research trends. Additionally, we highlight interdisciplinary knowledge flows, the role of bridging authors, and the evolving involvement of academia, industry, and research centers. Our holistic analysis provides a comprehensive view of computing research communities, their structural evolution, and the factors driving their collective growth.