<ul data-eligibleForWebStory="true"><li>The exponential growth in scientific publications is a challenge for researchers to discover impactful research ideas and collaborations outside their field.</li><li>Predicting a scientific paper's future citation counts usually occurs after the research is completed, limiting the ability to anticipate impact at the idea stage.</li><li>Researchers have developed a large evolving knowledge graph utilizing over 21 million scientific papers to predict the impact of new research ideas that have not yet been published.</li><li>The knowledge graph combines a semantic network from paper content and an impact network from historic paper citations.</li><li>Machine learning techniques have enabled accurate prediction of the evolving network's dynamics into the future with high accuracy, with AUC values exceeding 0.9 in most cases.</li><li>The goal is to forecast the impact of new research directions, providing insights into potential new and impactful scientific ideas before they are published.</li></ul>

Forecasting high-impact research topics via machine learning on evolving knowledge graphs

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