Analysis of Research Trends in Computer Science: A Network Approach

TL;DR

This paper analyzes research trends in computer science using a network approach, revealing key interdisciplinary fields and subfields, and providing insights into the structure and focus areas of CS research in 2022.

Contribution

It introduces a network-based analysis of CS research fields and subfields, highlighting interdisciplinarity and identifying key areas like machine learning and privacy.

Findings

Computing methodologies and privacy/security are highly interdisciplinary.

Human-centered computing has the highest publication frequency.

Machine learning is the most interdisciplinary and multidisciplinary subfield.

Abstract

Nowadays, computer science (CS) has emerged as a dominant force in numerous research areas both within and beyond its own discipline. However, despite its significant impact on scholarly space, only a limited number of studies have been conducted to analyze the research trends and relationships within computer science. In this study, we collected information on fields and subfields from over 2,000 research articles published in the 2022 proceedings of the top Association for Computing Machinery (ACM) conferences spanning various research fields. Through a network approach, we investigated the interconnections between CS fields and subfields to evaluate their interdisciplinarity and multidisciplinarity. Our findings indicate that computing methodologies and privacy and security stand out as the most interdisciplinary fields, while human-centered computing exhibits the highest frequency among the papers. Furthermore, we discovered that machine learning emerges as the most interdisciplinary and multidisciplinary subfield within computer science. These results offer valuable insights for universities seeking to foster interdisciplinary research opportunities for their students.