Decoupling User Relationships Guides Information Diffusion Prediction (Student Abstract)
Wenxue Ye, Shichong Li, Zhangtao Cheng, Xovee Xu, Ting Zhong, Bei Hui, Fan Zhou
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.
BibTeX
@article{Ye_Li_Cheng_Xu_Zhong_Hui_Zhou_2024, title={Decoupling User Relationships Guides Information Diffusion Prediction (Student Abstract)}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30530}, DOI={10.1609/aaai.v38i21.30530}, abstractNote={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.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Ye, Wenxue and Li, Shichong and Cheng, Zhangtao and Xu, Xovee and Zhong, Ting and Hui, Bei and Zhou, Fan}, year={2024}, month={Mar.}, pages={23696-23698} }