Forecasting high-impact research topics via machine learning on evolving knowledge graphs
This addresses the problem of information overload for researchers by enabling early prediction of high-impact topics, though it is incremental as it builds on existing citation prediction methods.
The authors tackled the challenge of discovering impactful research ideas early by predicting the impact of unpublished idea onsets using a large evolving knowledge graph built from over 21 million papers, achieving high accuracy with AUC values beyond 0.9 in most experiments.
The exponential growth in scientific publications poses a severe challenge for human researchers. It forces attention to more narrow sub-fields, which makes it challenging to discover new impactful research ideas and collaborations outside one's own field. While there are ways to predict a scientific paper's future citation counts, they need the research to be finished and the paper written, usually assessing impact long after the idea was conceived. Here we show how to predict the impact of onsets of ideas that have never been published by researchers. For that, we developed a large evolving knowledge graph built from more than 21 million scientific papers. It combines a semantic network created from the content of the papers and an impact network created from the historic citations of papers. Using machine learning, we can predict the dynamic of the evolving network into the future with high accuracy (AUC values beyond 0.9 for most experiments), and thereby the impact of new research directions. We envision that the ability to predict the impact of new ideas will be a crucial component of future artificial muses that can inspire new impactful and interesting scientific ideas.