@inproceedings{xu-etal-2025-hyperidp,
    title = "{H}yper{IDP}: Customizing Temporal Hypergraph Neural Networks for Multi-Scale Information Diffusion Prediction",
    author = "Xu, Haowei  and
      Gao, Chao  and
      Li, Xianghua  and
      Wang, Zhen",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.coling-main.64/",
    pages = "964--977",
    abstract = "Information diffusion prediction is crucial for understanding how information spreads within social networks, addressing both macroscopic and microscopic prediction tasks. Macroscopic prediction assesses the overall impact of diffusion, while microscopic prediction focuses on identifying the next user likely to be influenced. However, few studies have focused on both scales of diffusion. This paper presents HyperIDP, a novel Hypergraph-based model designed to manage both macroscopic and microscopic Information Diffusion Prediction tasks. The model captures interactions and dynamics of cascades at the macro level with hypergraph neural networks (HGNNs) while integrating social homophily at the micro level. Considering the diverse data distributions across social media platforms, which necessitate extensive tuning of HGNN architectures, a search space is constructed to accommodate diffusion hypergraphs, with optimal architectures derived through differentiable search strategies. Additionally, cooperative-adversarial loss, inspired by multi-task learning, is introduced to ensure that the model can leverage the advantages of the shared representation when handling both tasks, while also avoiding potential conflicts. Experimental results show that the proposed model significantly outperforms baselines."
}