用多模态图模型提升供应链匹配准确率
C-MAG: Cascade Multimodal Attributed Graphs for Supply Chain Link Prediction
- 分两阶段融合文本与图像特征，构建制造商-产品关联嵌入
- 在8888家厂商、7万+产品数据上实现高精度链接预测
- 适合做供应链智能匹配的研究者与工业界应用者
连接不断增长的产品与合适制造商和供应商对构建弹性高效的全球供应链至关重要，但传统方法难以捕捉真实制造商档案中复杂的资质能力、认证要求、地理限制及丰富的多模态数据。为此，我们构建了PMGraph，一个公开的双向异质多模态供应链图谱，包含8,888家制造商、70,000+产品、110,000+制造商-产品边以及29,000+产品图像。基于此基准，我们提出级联多模态属性图C-MAG，一种两阶段架构：首先对齐并聚合文本与视觉属性生成中间群组嵌入，再通过多尺度消息传递在制造商-产品异构图中传播信息，显著提升链接预测性能。C-MAG还提供了面向模态感知融合的实用指导，在噪声环境下仍保持良好表现。
原文摘要 · Abstract (English)
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.
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