Analyzing Paper Citation Trend of Popular Research Fields

Abstract

The ever-expanding volume and diversity of scientific literature pose a significant challenge for researchers in detecting emerging, current, and future research trends. A trend represents the prevailing direction of research within a defined timeframe. Detecting trends involves identifying areas of growing interest over time, while trend analysis involves gathering data and discerning patterns. Despite the utilization of diverse methods for analyzing and identifying trends in scientific research, there remains a lack of comprehensive understanding regarding the significance of following research trends for citation of research papers. The objective of this research is to examine the significance of monitoring trends in Computer Science (CS) research, the influence of aligning with these trends on paper citations, and the correlation in citation patterns among papers within the CS domain. We analyze trends in CS conference papers and the evolution of research fields from 1985 to 2017 using the Microsoft Academic Graph (MAG) dataset of CS papers in the L1 field of study (FoS). Our experimental findings reveal that Data Mining, Artificial Intelligence, Computer Vision, Machine Learning, and Database research exhibit the highest publication trends. Additionally, our results suggest that papers within the same field demonstrate similar citation trends.