结合层次图神经网络与长短期记忆的产业链风险评估预警模型

doi:10.11772/j.issn.1001-9081.2023101387

《计算机应用》唯一官方网站 ›› 2024, Vol. 44 ›› Issue (10): 3223-3231.DOI: 10.11772/j.issn.1001-9081.2023101387

结合层次图神经网络与长短期记忆的产业链风险评估预警模型

花晓雨¹, 李冬芬¹, 付优¹, 毕可骏², 应时³, 王瑞锦⁴()

^1.成都理工大学计算机与网络安全学院（示范性软件学院），成都 610059
^2.四川大学计算机学院，成都 610065
^3.武汉大学计算机学院，武汉 430072
^4.电子科技大学信息与软件工程学院，成都 610054

收稿日期:2023-10-17 修回日期:2024-01-29 接受日期:2024-02-05 发布日期:2024-10-15 出版日期:2024-10-10
通讯作者: 王瑞锦
作者简介:花晓雨（1999—），男，河南信阳人，硕士研究生，CCF会员，主要研究方向：图神经网络、产业链风险评估预警
李冬芬（1979—），女，青海海东人，副教授，博士，CCF会员，主要研究方向：人工智能、风险评估与预警、量子密码通信
付优（1999—），男，四川成都人，硕士研究生，主要研究方向：产业链风险评估预警、量子密码通信
毕可骏（1982—），男，四川德阳人，高级工程师，硕士，主要研究方向：工业互联网、人工智能、产业链知识图谱构建
应时（1965—），男，湖北武汉人，教授，博士，CCF会员，主要研究方向：大数据高效处理与智能分析、机器学习、人工智能、产业链知识图谱构建
王瑞锦（1980—），男，甘肃天水人，副教授，博士，CCF会员，主要研究方向：边缘计算、人工智能、信息加密、产业链风险评估 ruijinwang@uestc.edu.cn。
基金资助:
国家重点研发计划项目(2022YFB3304300)

Industrial chain risk assessment and early warning model combining hierarchical graph neural network and long short-term memory

Xiaoyu HUA¹, Dongfen LI¹, You FU¹, Kejun BI², Shi YING³, Ruijin WANG⁴()

^1.College of Computer Science and Cyber Security （Pilot Software College），Chengdu University of Technology，Chengdu Sichuan 610059，China
^2.College of Computer Science，Sichuan University，Chengdu Sichuan 610065，China
^3.School of Computer Science，Wuhan University，Wuhan Hubei 430072，China
^4.School of Information and Software Engineering，University of Electronic Science and Technology of China，Chengdu Sichuan 610054，China

Received:2023-10-17 Revised:2024-01-29 Accepted:2024-02-05 Online:2024-10-15 Published:2024-10-10
Contact: Ruijin WANG
About author:HUA Xiaoyu， born in 1999， M. S. candidate. His research interests include graph neural network， industrial chain risk assessment and early warning.
LI Dongfen， born in 1979， Ph. D.， associate professor. Her research interests include artificial intelligence， risk assessment and early warning， quantum cryptographic communication.
FU You， born in 1999， M. S. candidate. His research interests include industrial chain risk assessment and early warning， quantum cryptographic communication.
BI Kejun， born in 1982， M. S.， senior engineer. His research interests include industrial Internet， artificial intelligence， construction of industrial chain knowledge graph.
YING Shi， born in 1965， Ph. D.， professor. His research interests include efficient processing and intelligent analysis of big data， machine learning， artificial intelligence， construction of industrial chain knowledge graph.
Supported by:
National Key R&D Program of China(2022YFB3304300)

摘要/Abstract

摘要：

产业链风险评估预警是有效保护产业链上下游公司利益和减轻公司风险的重要措施。然而，现有方法由于忽视了产业链上下游公司之间的传播效应和公司信息的不透明性，无法准确评估公司风险，且忽略了动态财务数据对产业链的影响，无法提前感知风险，进行风险预警。针对以上问题，提出一种结合层次图（HG）神经网络与长短期记忆（LSTM）的产业链风险评估预警模型（HiGNN）。首先，利用产业链上下游关系和投融资关系构建“产业链-投资”HG；其次，利用财务特征提取模块提取公司多季度财务数据的特征；再次，利用投资特征提取模块提取投资关系图特征；最后，利用注意力机制融合财务特征和投资特征，通过图表示学习方法对公司节点进行风险分类。在真实的集成电路制造业数据集上的实验结果表明，与图注意力网络（GAT）模型、循环神经网络（RNN）模型相比，当训练比率为60%时，所提模型的准确率分别提升了14.87%、22.10%，F1值提升了12.63%、16.67%。所提模型能够有效捕捉产业链中的传染效应，提高风险识别能力，优于传统的机器学习方法和图神经网络方法。

关键词: 产业链风险评估, 层次图神经网络, 长短期记忆网络, 财务特征提取, 投资特征提取

Abstract:

Industrial chain risk assessment and early warning are essential measures to effectively protect the interests of upstream and downstream companies in the industrial chain and mitigate company risks. However， existing methods often need to pay more attention to the propagation effects between upstream and downstream companies in the industrial chain and the opacity of company information， resulting in inaccurate risk assessment of companies and the failure to perceive risks in advance for early warning. To address the above problems， the Hierarchical Graph Neural Network （HiGNN）， an industrial chain risk assessment and early warning model that combined Hierarchical Graph （HG） neural network and Long Short-Term Memory （LSTM）， was proposed. Firstly， an “industrial chain-investment” HG was constructed based on the relationships between upstream and downstream companies and investment activities. Then， a financial feature extraction module was utilized to extract features from multi-quarter financial data of companies， while an investment feature extraction module was utilized to extract features from the investment relationship graph. Finally， an attention mechanism was employed to integrate the financial features with the investment features， enabling risk classification of company nodes through graph representation learning methods. The experimental results on a real integrated circuit manufacturing dataset showed that compared with the Graph ATtention network （GAT） model and the Recurrent Neural Network （RNN） model， the accuracy of the proposed model increased by 14.87% and 22.10%， and the F1-score increased by 12.63% and 16.67% with the 60% training ratio. The proposed model can effectively capture the contagion effect in the industrial chain and improve risk identification capability， which is superior to traditional machine learning methods and graph neural network methods.

Key words: industrial chain risk assessment, Hierarchical Graph (HG) neural network, Long Short-Term Memory (LSTM) network, financial feature extraction, investment feature extraction

中图分类号:

TP391.4

花晓雨, 李冬芬, 付优, 毕可骏, 应时, 王瑞锦. 结合层次图神经网络与长短期记忆的产业链风险评估预警模型[J]. 计算机应用, 2024, 44(10): 3223-3231.

Xiaoyu HUA, Dongfen LI, You FU, Kejun BI, Shi YING, Ruijin WANG. Industrial chain risk assessment and early warning model combining hierarchical graph neural network and long short-term memory[J]. Journal of Computer Applications, 2024, 44(10): 3223-3231.

图/表 10

图1 HiGNN架构

Fig. 1 HiGNN architecture

图2 “产业链-投资”层次图

Fig. 2 “Industrial chain?investment” hierarchical graph

图3 LSTM自编码器结构

Fig. 3 LSTM autoencoder structure

图4 单个LSTM单元

Fig. 4 Single LSTM unit

表1 数据规模

Tab. 1 Data size

名称	值
层次图节点数	6 963
层次图边数	128 326
产业链图节点数	1 700
产业链图边数	83 052
投资图数	1 700
投资图节点数	45 263
财务指标数	18
财务指标季度数	49

表2 对比实验结果

Tab. 2 Comparison experiment results

模型	训练比率/%	F1-score	AUC	Accuracy
XGBoost	60	0.554	0.657	0.521
	40	0.550	0.653	0.520
	20	0.548	0.647	0.514
GCN	60	0.582	0.694	0.562
	40	0.575	0.688	0.560
	20	0.559	0.691	0.558
GAT	60	0.578	0.672	0.558
	40	0.572	0.667	0.553
	20	0.568	0.653	0.547
GraphSAGE	60	0.585	0.690	0.568
	40	0.579	0.685	0.559
	20	0.563	0.682	0.558
RNN	60	0.558	0.652	0.525
	40	0.547	0.656	0.518
	20	0.535	0.653	0.501
Transformer	60	0.564	0.663	0.531
	40	0.549	0.658	0.514
	20	0.524	0.650	0.505
HiGNN	60	0.651	0.736	0.641
	40	0.650	0.729	0.631
	20	0.641	0.722	0.627

图 5 不同模型下邻居节点分类结果的变化率

Fig. 5 Change rates of neighbor node classification results under different models

图 6 扰动财务特征后各模型F1值相对变化

Fig. 6 Relative change in F1-score of each model after perturbing financial features

图 7 扰动投资特征后各模型F1值相对变化

Fig. 7 Relative change in F1-score of each model after perturbing investment features

表3 消融实验结果

Tab. 3 Results of ablation experiments

模型	F1-score	AUC	Accuracy
HiGNN\lstm	0.605	0.698	0.597
HiGNN\invest	0.621	0.702	0.612
HiGNN	0.650	0.721	0.627

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结合层次图神经网络与长短期记忆的产业链风险评估预警模型

Industrial chain risk assessment and early warning model combining hierarchical graph neural network and long short-term memory

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