Decoupling User Relationships Guides Information Diffusion Prediction (Student Abstract)
Abstract
Information diffusion prediction is a critical task for many social network applications. However, current methods are mainly limited by the following aspects: user relationships behind resharing behaviors are complex and entangled. To address these issues, we propose MHGFormer, a novel multi-channel hypergraph transformer framework, to better decouple complex user relations and obtain fine-grained user representations. First, we employ designed triangular motifs to decouple user relations into three different level hypergraphs. Second, a position-aware hypergraph transformer is used to refine user relation and obtain high-quality user representations. Extensive experiments conducted on two social datasets demonstrate that MHGFormer outperforms state-of-the-art diffusion models across several settings.
Cite
Text
Ye et al. "Decoupling User Relationships Guides Information Diffusion Prediction (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30530Markdown
[Ye et al. "Decoupling User Relationships Guides Information Diffusion Prediction (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/ye2024aaai-decoupling/) doi:10.1609/AAAI.V38I21.30530BibTeX
@inproceedings{ye2024aaai-decoupling,
title = {{Decoupling User Relationships Guides Information Diffusion Prediction (Student Abstract)}},
author = {Ye, Wenxue and Li, Shichong and Cheng, Zhangtao and Xu, Xovee and Zhong, Ting and Hui, Bei and Zhou, Fan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23696-23698},
doi = {10.1609/AAAI.V38I21.30530},
url = {https://mlanthology.org/aaai/2024/ye2024aaai-decoupling/}
}