Risk Assessment Model for Industrial Chain Based on Neighbor Sampling and Graph Attention Mechanism
Risk assessment is an important way to improve the resilience of the industrial chain and also an effective method to re-duce the instability of the industrial chain.However,existing research on risk assessment is based on supply chain structure and neglects other factors,which can not accurately depict the correlation between upstream and downstream nodes in the industrial chain,resulting in biased evaluation results.In response to the above issues,considering the interconnected nature of various nodes within the industry chain,diverse risk situations,and the existence of risk transmission,this paper proposes an industry chain risk assessment model based on graph attention mechanism and neighbor sampling(GANS).Firstly,a heterogeneous graph of the industrial chain is constructed,using"product-company"and"product-product"to depict the correlation between nodes in the industrial chain,and financial data and other data features are extracted from the industrial chain as nodes'data features.Se-condly,a company relationship graph generation module based on meta paths and company investment and financing association rules is proposed to achieve efficient transformation of company node relationships and efficient learning of structural features in the industrial chain.Next,an industry chain risk assessment module based on neighbor sampling and graph attention mechanism is designed for various generated company graphs.The features of node neighbors are randomly sampled and aggregated,and at-tention mechanism is used to adaptively aggregate node features based on multiple company graphs.Through the classifier,node-level risk assessment is realized.Finally,risk assessment of the industrial chain is conducted based on the risk level and structural features of nodes.Experiments show that GANS outperforms existing models in terms of accuracy and Fl scores on real indu-strial chain datasets.These results demonstrate the accuracy and effectiveness of GANS in implementing industrial chain risk as-sessment.
Industry chainRisk assessmentHeterogeneous graphNeighbourhood samplingGraph attention mechanism
NETL
NSTL
万方数据
