Explainable Forecasting of Scientific Breakthroughs from Concept Network Dynamics
We introduce an explainable machine-learning approach that forecasts the structural precursors of scientific breakthroughs -- the emergence and intensification of links between research concepts -- by modelling how OpenAlex concept networks evolve over time. Using 59 semantic and topological features, a two-stage LightGBM model jointly predicts the formation and the future weight of concept pairs, adding a regression stage that quantifies expected intensity to prior link-existence forecasts. Relative to the state of the art, the approach improves accuracy and explainability at once: comparative validation across four technology and biomedical domains yields ROC-AUC in [0.954, 0.967] at al...
| Authors | Thomas Maillart, Thibaut Chataing, Ntorina Antoni et al. |
|---|---|
| Primary category | cs.SI |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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