<p>Weakly supervised pathological image segmentation aims to localize disease regions using coarse annotations (e.g., image-level labels), which significantly reduces annotation cost but introduces challenges such as imprecise localization and noisy predictions. In this paper, we propose multi-scale patch graph reasoning network (<b>MPGR-Net</b>), a novel framework that integrates multi-scale graph reasoning into transformer-based architectures to address these limitations. Specifically, we first employ a Vision Transformer encoder to extract global-aware patch representations. To explicitly model structured spatial dependencies that are often overlooked by standard self-attention, we introduce a <i>multi-scale patch graph reasoning module</i>, where patch tokens are treated as graph nodes and connected via predefined spatial adjacency at multiple scales. This design enables effective aggregation of local and long-range contextual information while maintaining computational efficiency. Furthermore, we incorporate a lightweight cross-attention mechanism to enhance global-to-local feature interaction through channel-wise modulation. Extensive experiments on pathological image segmentation benchmarks demonstrate that MPGR-Net consistently outperforms existing weakly supervised methods, achieving superior segmentation accuracy and robustness. Ablation studies further validate the effectiveness of the proposed graph reasoning module.</p>