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However, existing propagation\u2010based rumor detection models often overlook the uncertainty of the underlying propagation structure and typically require a large amount of labeled data for training. To address these challenges, we propose a novel rumor detection framework, namely, the Uncertainty\u2010Inference Contrastive Learning (UICL) model. Specifically, UICL innovatively incorporates an edge\u2010wise augmentation strategy into the general contrastive learning framework, including an edge\u2010inference augmentation component and an EdgeDrop augmentation component, which primarily aim to capture the edge uncertainty of the propagation structure and alleviate the sparsity problem of the original dataset. A new negative sampling strategy is also introduced to enhance contrastive learning on rumor propagation graphs. Furthermore, we use labeled data to fine\u2010tune the detection module. 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