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Pixel-level annotation for skin lesion segmentation is costly and subjective. This limits large-scale clinical deployment. Weakly supervised methods based on image-level labels alleviate this burden. However, most existing methods rely on class activation maps (CAMs), which often fail to cover the entire lesion. To address this, we introduce a Frequency and Geometry Guided Graph Clustering framework, reformulating weakly supervised skin lesion segmentation as a differentiable node-clustering problem. First, a Frequency-aware Feature Modulation module injects local spectral priors into deep features. This enhances the model’s sensitivity to ambiguous boundaries. Second, we construct a semantic-spatial graph from these features. A graph neural network (GNN) then captures long-range dependencies to ensure structural completeness. Finally, we propose a tri-source fusion strategy that integrates three cues (GNN cluster, CAM, and the input image) via an improved side-window mean filtering. Experiments on ISIC 2017, ISIC 2018, and PH$^2$ show that our method achieves state-of-the-art performance. It significantly narrows the gap with fully supervised models, providing a reliable solution for label-efficient skin lesion analysis.