Introduction and Motivation

The Chinese University of Hong Kong is a major research institution in the education sector in Hong Kong, hosting many departments, schools, and programs. Students can acquire different degrees from their departments, which give in sum an impression of the state of research and the ongoing topics of departments and their field. Like society, this research is subject to dynamic changes and dependent on innovations in methodology, technology, new discoveries, but also societal trends, financial resources, among others; in short, it evolves over time. In our research, we aimed at investigating the network of research topics shared by departments.

The idea for this project also originated to some degree from reflections about our own work and studies in sociology, and the diverse interests from our fellow postgraduate colleagues. Our goal was to shed light on the existing interdisciplinarity in a modern research context, where students – and staff – might not always be aware that they share interests, methods, and strategies with peers from other departments. It is our belief that in a fast-paced and increasingly complex world, maintaining strict disciplinary boundaries could in some cases arguably complicate the best possible outcome or leave some potential for further research unused. Hence, intellectual openness and competing scientific arguments should be welcomed and seen as an opportunity, rather than a challenge.

A few words on the structure: in Data and Methods, in the first parts, the process of retrieving, storing and transforming our data will be explained, followed by some descriptive numbers. In the second part, we will briefly talk about the further steps from the textual information to the topic modeling. In Visualizations, we show the graphical results of our analysis in a number of networks. We presented the aggregated network, important sub-networks, and the departmental connections across institutional partitions. Finally, you can download our visualization scripts and the data used for the networks under Python Codes.

So we hope, dear reader, you’ll enjoy scrolling through our website. We also greatly welcome your feedback.

CAO Ji (PhD Soci/4) & Raphael Duerr (PhD Soci/3)