Rapid and accurate disaster classification is critical for effective emergency response and resource allocation. Existing approaches to multimodal crisis classification typically presume that the textual and visual elements of a tweet share the same label, thereby overlooking frequent mismatches between modalities and neglecting the contextual links that span multiple tweets. To address these limitations, we introduce GNN-MCC, a novel Graph Neural Network based framework for Multimodal Crisis Classification. Our method constructs a unified graph representation in which individual tweets are decomposed into separate text and image nodes, allowing the model to capture complex intra- and inter-modal relationships. To further reconcile discrepancies between modalities, we incorporate a contrastive attention mechanism that aligns complementary information across text and image channels. Empirical evaluation on the publicly available CrisisMMD dataset demonstrates that GNN-MCC substantially outperforms state-of-the-art baselines under both label-consistent and label-inconsistent conditions. The source code for this paper is available at https://github.com/kliean/GNN-MCC .