Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (10): 218-226.doi: 10.11896/jsjkx.230900145

• Database & Big Data & Data Science • Previous Articles     Next Articles

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

SUN Pengzhao1, BI Kejun2, TANG Chao3, LI Dongfen4, YING Shi5, WANG Ruijin1   

  1. 1 School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China
    2 School of Computer Science,Sichuan University,Chengdu 610065,China
    3 Sichuan Changhong Electronic Holding Group Co.Ltd,Mianyang,Sichuan 621000,China
    4 College of Computer Science and Cyber Security,Chengdu University of Technology,Chengdu 610059,China
    5 School of Computer Science,Wuhan University,Wuhan 430072,China
  • Received:2023-09-26 Revised:2024-03-08 Online:2024-10-15 Published:2024-10-11
  • About author:SUN Pengzhao,born in 1999,postgra-duate,is a member of CCF(No.67770G).His main research interests include graph neural network and artificial intelligence.
    WANG Ruijin,born in 1980,Ph.D,associate professor,is a member of CCF(No.22983S).Hismain research intere-sts include edge computing,artificial intelligence and information security.
  • Supported by:
    National Key R&D Program of China(2022YFB3304300,2022YFB3304303).

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Abstract: 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.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 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 indu-strial chain datasets.These results demonstrate the accuracy and effectiveness of GANS in implementing industrial chain risk assessment.

Key words: Industry chain, Risk assessment, Heterogeneous graph, Neighbourhood sampling, Graph attention mechanism

CLC Number: 

  • TP391
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