HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessment

• URL: http://arxiv.org/abs/2311.04244v1
• Date: Tue, 7 Nov 2023 00:54:04 GMT
• Title: HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessment
• Authors: Zhanting Zhou, Kejun Bi, Yuyanzhen Zhong, Chao Tang, Dongfen Li, Shi Ying, Ruijin Wang
• Abstract summary: We propose a hierarchical knowledge transferable graph neural network-based (HKTGNN) supply chain risk assessment model. We embed the supply chain network corresponding to individual goods in the supply chain using the graph embedding module. Our model outperforms in experiments on a real-world supply chain dataset.
• Score: 3.439495194421287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The strength of a supply chain is an important measure of a country's or region's technical advancement and overall competitiveness. Establishing supply chain risk assessment models for effective management and mitigation of potential risks has become increasingly crucial. As the number of businesses grows, the important relationships become more complicated and difficult to measure. This emphasizes the need of extracting relevant information from graph data. Previously, academics mostly employed knowledge inference to increase the visibility of links between nodes in the supply chain. However, they have not solved the data hunger problem of single node feature characteristics. We propose a hierarchical knowledge transferable graph neural network-based (HKTGNN) supply chain risk assessment model to address these issues. Our approach is based on current graph embedding methods for assessing corporate investment risk assessment. We embed the supply chain network corresponding to individual goods in the supply chain using the graph embedding module, resulting in a directed homogeneous graph with just product nodes. This reduces the complicated supply chain network into a basic product network. It addresses difficulties using the domain difference knowledge transferable module based on centrality, which is presented by the premise that supply chain feature characteristics may be biased in the actual world. Meanwhile, the feature complement and message passing will alleviate the data hunger problem, which is driven by domain differences. Our model outperforms in experiments on a real-world supply chain dataset. We will give an equation to prove that our comparative experiment is both effective and fair.

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