C‑MAG: Cascade Multimodal Attributed Graphs for Supply Chain Link Prediction

Yunqing Li, Zixiang Tang, Jiaying Zhuang, Zhenyu Yang, Farhad Ameri, Jianbang Zhang

Keywords: Graph Neural Network, Link Prediction, Multimodal Knowledge Graph, Graph Embedding, Manufacturer Relationship

TL;DR: We introduce PMGraph—a large‐scale, multimodal supply‑chain graph benchmark—and C‑MAG, a two‑stage cascade model that fuses textual and visual attributes to significantly improve manufacturer–product link prediction in noisy, real‑world settings.

Abstract: 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 heterograph 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.

Submission Number: 17