Interpretable Transformer Hawkes Processes: Unveiling Complex Interactions in Social Networks
<|reference_start|>Interpretable Transformer Hawkes Processes: Unveiling Complex Interactions in Social Networks: Social networks represent complex ecosystems where the interactions between users or groups play a pivotal role in information dissemination, opinion formation, and social interactions. Effectively harnessing event sequence data within social networks to unearth interactions among users or groups has persistently posed a challenging frontier within the realm of point processes. Current deep point process models face inherent limitations within the context of social networks, constraining both their interpretability and expressive power. These models encounter challenges in capturing interactions among users or groups and often rely on parameterized extrapolation methods when modelling intensity over non-event intervals, limiting their capacity to capture intricate intensity patterns, particularly beyond observed events. To address these challenges, this study proposes modifications to Transformer Hawkes processes (THP), leading to the development of interpretable Transformer Hawkes processes (ITHP). ITHP inherits the strengths of THP while aligning with statistical nonlinear Hawkes processes, thereby enhancing its interpretability and providing valuable insights into interactions between users or groups. Additionally, ITHP enhances the flexibility of the intensity function over non-event intervals, making it better suited to capture complex event propagation patterns in social networks. Experimental results, both on synthetic and real data, demonstrate the effectiveness of ITHP in overcoming the identified limitations. Moreover, they highlight ITHP's applicability in the context of exploring the complex impact of users or groups within social networks.<|reference_end|>
@article{meng2024interpretable,
    title={Interpretable Transformer Hawkes Processes: Unveiling Complex
  Interactions in Social Networks},
    author={Zizhuo Meng, Ke Wan, Yadong Huang, Zhidong Li, Yang Wang, Feng Zhou},
    journal={arXiv preprint arXiv:2405.16059},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16059},
    primaryClass={cs.SI}
}