  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.
