多模块注意力协同的异构图神经网络产业链风险识别模型

Industrial Chain Risk Identification Model Based on Heterogeneous Graph Neural Network with Multi-module Attention Collaboration

摘要

风险识别通过精准定位产业链的脆弱节点以阻断风险传导,对于维护产业链完整性至关重要.然而,现有方法缺乏对产业链节点进行静态指标级、动态时序级和结构空间级的协同建模.因此,本文提出一种多模块注意力协同的异构图神经网络产业链风险识别模型(MGRI).MGRI首先根据企业所属行业动态学习并分配各属性维度的权重,以突出关键指标的重要性;同时捕捉低频财务时间序列蕴含的风险演变信息,形成全局动态时序表征;最后以动静态融合特征作为初始表示聚合关系感知的邻域信息,生成包含产业链结构空间依赖的最终嵌入并用于风险识别,实现了跨维度多层级特征的协同建模.实验验证了MGRI在多个真实产业链数据集上准确识别风险企业方面优于最先进的方法.

Abstract

Risk identification is essential for maintaining the integrity of industry chains by precisely locating vulnerable nodes to interrupt risk propagation.However,existing methods lack collaborative modeling of industry nodes across static-indicator level,dynamic-time-series level,and structural-spatial level representations.Therefore,this paper proposes an industrial chain risk identification model based on a heterogeneous graph neural network with multi-module attention collaboration(MGRI).MGRI first dynamically learns and assigns weights to attribute dimensions based on a company′s industry to emphasize the importance of key indicators;it simultaneously captures the risk evolution information embedded in low-frequency financial time series to form a global dynamic temporal representation; finally,using the fused static and dynamic features as initial representations,it aggregates relation-aware neighborhood information to generate the final embeddings that contain the structural-spatial dependencies of the industry chain for risk identification,thereby achieving collaborative modeling of cross-dimensional,multi-level features.Experiments on two real-world industry chain datasets demonstrate that MGRI outperforms state-of-the-art methods in accurately identifying at-risk enterprises.

关键词

产业链 / 风险识别 / 异构图神经网络 / 注意力机制 / 多模块协同

Key words

industry chain / risk identification / heterogeneous graph neural network / attention mechanism / multi-module collaboration

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基金资助

国家重点研发计划项目(2022YFB3304300)资助;国家自然科学基金项目(62472329,62072342)资助.