This brief proposes a novel Virtual-Geography Hawkes Process (VG-Hawkes) to model citation dynamics considering academic networks. The VG-Hawkes model incorporates academic relationships between authors as virtual distances by extending the conventional temporal Hawkes process, enabling a more detailed and realistic representation of citation behavior. Validation results on real-world datasets show that the VG-Hawkes model consistently achieves higher log-likelihood scores than temporal Hawkes models and effectively captures citation peaks and distributional patterns. While this study focuses on selected datasets and pairwise interactions, the model is general and readily extensible. Future work includes scaling to broader datasets and incorporating more complex author relationships. The VG-Hawkes model provides a novel and flexible framework for academic network analysis and scientific impact prediction.
Ganeshbabu et al. (Fri,) studied this question.