基于邻居采样和图注意力机制的产业链风险评估模型

Risk Assessment Model for Industrial Chain Based on Neighbor Sampling and Graph Attention Mechanism

风险评估是提高产业链韧性的重要途径,也是降低产业链不稳定性的有效手段。然而,现有风险评估的研究基于供应链结构,忽略了其他因素,无法准确地刻画产业链上下游各节点的关联关系,导致评估效果存在偏差。针对上述问题,考虑到产业链内部各节点相互关联、风险状况多样、存在风险传递的特性,提出了基于邻居采样和图注意力机制的产业链风险评估模型GANS。首先,构建了产业链的异质图,利用“产品-公司”“产品-产品”刻画了产业链节点之间的关联关系,并从产业链中提取财务数据等作为节点的数据特征;其次,提出了基于元路径和公司投融资关联规则的公司关系图生成模块,实现对产业链中公司节点关系的高效转化和结构特征的高效学习;接着,针对生成的多种公司关系图,设计了结合邻居采样和图注意力机制的产业链风险评估模块,对节点邻域特征进行随机采样和聚合,同时结合注意力机制对基于多种公司图的节点特征进行自适应聚合,并通过分类器实现节点级风险评估;最后,依据节点风险等级与节点的结构特征对产业链进行风险评估。实验表明,在真实产业链数据集上,GANS在准确性、F1分数等方面均优于现有的模型,证明了GANS实现产业链风险评估的准确性和有效性。

Risk assessment is an important way to improve the resilience of the industrial chain and also an effective method to reduce 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.Secondly,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 attention 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 F1 scores on real industrial chain datasets.These results demonstrate the accuracy and effectiveness of GANS in implementing industrial chain risk assessment.