Authors
- Weikang He Chongqing University of Posts and Telecommunications
- Yunpeng Xiao Chongqing University of Posts and Telecommunications
- Mengyang Huang Chongqing University of Posts and Telecommunications
- Xuemei Mou Chongqing University of Posts and Telecommunications
- Rong Wang Chongqing University of Posts and Telecommunications
- Qian Li Chongqing University of Posts and Telecommunications
DOI:
https://doi.org/10.1609/aaai.v40i1.37000Abstract
Information diffusion prediction is crucial for understanding social network dynamics, yet existing methods often neglect user participation uncertainty. This oversight typically stems from an implicit participation homogeneity assumption, which treats all observed interactions as equally reliable propagation signals, leading to fragile inferred topologies and uncertainty contamination. To address this, we propose SIEVE, a novel framework employing two synergistic strategies. First, robust node representations are learned via controllable uncertainty injection coupled with associated contrastive learning, mitigating topological fragility. Second, an uncertainty-aware directed graph aggregation mechanism is introduced, which dynamically constructs asymmetric aggregation topologies with adaptive weighting, thereby suppressing uncertainty contamination. Experiments on four public datasets demonstrate that SIEVE significantly outperforms state-of-the-art methods, offering valuable insights for designing robust information diffusion prediction models.
How to Cite
He, W., Xiao, Y., Huang, M., Mou, X., Wang, R., & Li, Q. (2026). Directing Uncertainty-Aware Information Flow for Robust Diffusion Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 381–389. https://doi.org/10.1609/aaai.v40i1.37000
Issue
Section
AAAI Technical Track on Application Domains I