Emerging Research Topics Identification Using Temporal Graph Neural Networks

Abstract

The dynamic landscape of research necessitates effective methods for the timely identification of emerging research topics, a critical pursuit for researchers and decision makers in both governmental and industrial spheres. Traditional approaches to this challenge have predominantly relied on retrospective analyses, limiting their applicability in real world scenarios where proactive foresight is paramount. This study addresses this constraint through the introduction of a novel methodology for the future prediction of emerging research topics, employing temporal graph neural networks. Our proposed framework revolves around the construction of co-word graphs, serving as input for our innovative machine learning model designed to forecast keyword frequencies in forthcoming time periods. To delineate emerging themes, keywords undergo clustering via a graph entropy algorithm that are subsequently sorted in terms of their “emergence score”. To validate the efficacy of our methodology, we apply it to forecast emerging research topics for the year 2022. The results showcase the potential of our approach, offering valuable insights into the trajectory of research themes poised to gain prominence in the near future.