DucDiff: Dual-Consistent Diffusion for Uncertainty-Aware Information Diffusion Prediction

Ting Zhong, Wenxue Ye, Shichong Li, Yang Liu, Zhangtao Cheng, Fan Zhou, Xueqin Chen

Abstract: Information diffusion prediction is a vital component for a wide range of social applications, including viral marketing identification and personal recommendation. Prior methods primarily focus on learning target user representation by modeling contextual information from the historical retweet user sequence of a single cascade, overlooking the uncertainties that exist in both historical propagation trajectory and future diffusion trends. In this work, we propose DucDiff, a novel dual-consistent diffusion model for enhancing target user representation used for information diffusion prediction. DucDiff harnesses the distribution generation capability of the diffusion model to generate target user representations from a distributional perspective rather than a fixed vector. Specifically, it captures the multi-latent aspects (i.e., uncertainties) of target user representation from historical and future user sequences, respectively, using disentangled dual denoising modules. Additionally, a shared information bottleneck is designed for the cross-distillation of knowledge between the historical and future denoising modules, eliminating the performance gap between training and inference, while ensuring that future information can be implicitly introduced during the inference phase. Extensive experiments conducted on five datasets demonstrate that DucDiff significantly outperforms state-of-the-art baselines.

External IDs:dblp:journals/tbd/ZhongYLLCZC25