C-MAG: Cascade Multimodal Attributed Graphs for Supply Chain Link Prediction
This work addresses the challenge of connecting products with manufacturers in global supply chains, which is incremental as it builds on existing graph-based methods with multimodal data.
The paper tackles the problem of predicting links between manufacturers and products in supply chains by introducing a new benchmark dataset and a two-stage multimodal graph architecture, achieving improved accuracy in link prediction.
Workshop version accepted at KDD 2025 (AI4SupplyChain). Connecting an ever-expanding catalogue of products with suitable manufacturers and suppliers is critical for resilient, efficient global supply chains, yet traditional methods struggle to capture complex capabilities, certifications, geographic constraints, and rich multimodal data of real-world manufacturer profiles. To address these gaps, we introduce PMGraph, a public benchmark of bipartite and heterogeneous multimodal supply-chain graphs linking 8,888 manufacturers, over 70k products, more than 110k manufacturer-product edges, and over 29k product images. Building on this benchmark, we propose the Cascade Multimodal Attributed Graph C-MAG, a two-stage architecture that first aligns and aggregates textual and visual attributes into intermediate group embeddings, then propagates them through a manufacturer-product hetero-graph via multiscale message passing to enhance link prediction accuracy. C-MAG also provides practical guidelines for modality-aware fusion, preserving predictive performance in noisy, real-world settings.