C-MAG：用于供应链链接预测的级联多模态属性图
C-MAG: Cascade Multimodal Attributed Graphs for Supply Chain Link Prediction
AI总结：
针对供应链中多模态数据建模难题，提出PMGraph基准和C-MAG两阶段架构，通过多尺度消息传递融合多模态属性以提升链接预测准确性。
摘要：
KDD 2025 (AI4SupplyChain) 研讨会版本已被接收。将不断扩展的产品目录与合适的制造商和供应商连接起来，对于构建有韧性、高效的全球供应链至关重要，然而传统方法难以捕捉真实世界制造商档案的复杂能力、认证、地理限制以及丰富的多模态数据。为了填补这些空白，我们引入了 PMGraph，这是一个公开基准，包含二分和异构的多模态供应链图，连接了 8,888 家制造商、超过 70k 种产品、超过 110k 条制造商-产品边以及超过 29k 张产品图像。在此基准之上，我们提出了 Cascade Multimodal Attributed Graph C-MAG，这是一种两阶段架构，首先将文本和视觉属性对齐并聚合为中间组嵌入，然后通过多尺度消息传递在制造商-产品异构图中传播它们，以提高链接预测的准确性。C-MAG 还为模态感知融合提供了实用指南，在嘈杂的真实世界环境中保持预测性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
