2024 Google Scholar Research Interest Ranking for Top 3260 Computer Science Authors
Atharva Rasane
TL;DR
The paper tackles the need for a current, data-driven map of computer science research interests among highly cited authors. It systematically collects Google Scholar author profiles with the scholarly library, maps self-reported interests to CSO categories using SentenceTransformers embeddings and cosine similarity, and visualizes the results. It reports the distribution and rankings of primary CS fields, notably AI, Software Engineering, and Data Mining, and demonstrates a scalable, ontology-based workflow for large-scale topic classification. The findings offer a benchmark of research priorities and a methodology for future analyses by researchers, institutions, and funders.
Abstract
Computer science research spans a diverse array of topics, with scholars exploring numerous subfields. This paper examines the self-reported research interests of the top 3,260 most cited computer science authors on Google Scholar. Using the scholarly Python library, we systematically retrieved and classified their interests into predefined categories based on the Computer Science Ontology (CSO). The analysis highlights a hierarchy of primary research areas, including Artificial Intelligence, Software Engineering, Data Mining, and Computer Systems. Additionally, it investigates the distribution of these interests, identifying emerging trends, established fields, and areas with relatively less attention. These findings provide a current snapshot of research priorities and serve as a foundation for guiding future studies in computer science.