Where is the Next Step? Predicting the Scientific Impact of Research Career

Predicting the scientific impact of research scholars is increasingly crucial for career planning, particularly for young scholars considering career transitions. However, predicting a scholar's future development, especially after they move to a different academic group, presents significant challenges. To tackle this issue, we propose a Future Publication Impact Prediction Network (FPIPN) based on graph neural networks. FPIPN leverages rich information from a heterogeneous academic graph for impact prediction. We employ a hierarchical attention mechanism to learn the significance of graph information and utilize a knowledge distillation strategy to assess future impact based on historical records. Extensive experiments on a real-world academic dataset showcase the effectiveness of our approach compared to state-of-the-art methods.