CPNS: Curriculum Learning-Guided Progressive Key Node Selection for Fake News Detection

Selecting valuable nodes from the social propagation graph is crucial for fake news detection. Previous work mainly evaluates node importance based on propagation depth, then constructs propagation subgraphs to mitigate noise interference. However, they still suffer from two limitations: (1) propagation subgraphs make signals from both valuable and noisy nodes smoother; (2) affected by depth, deep nodes are easily ignored. To address these issues, we propose Curriculum Learning-guided Progressive Key Node Selection for Fake News Detection (CPNS) to select key propagation nodes. Specifically, we propose a curriculum learning-guided progressive propagation strategy to select deep nodes. Moreover, we weight nodes based on properties, thereby distilling informative knowledge from selected nodes. Finally, we introduce multi-frequency consistency learning. It jointly models low-frequency propagation consensus and high-frequency local differences, thereby avoiding excessive smoothing of both noisy and valuable nodes. Experimental results demonstrate that our approach achieves state-of-the-art performance on two benchmark datasets, improving $\mathrm{Macro}\text{-}{F}_1$ by 1.54% and 2.15%, respectively.