Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks
作者机构：Center for Research on Intelligent Perception and ComputingNational Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China
出 版 物：《Machine Intelligence Research》 (机器智能研究（英文版）)
年 卷 期：2024年第21卷第2期
页 面：257-271页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Corporate credit rating hierarchical relation heterogeneous graph neural networks adversarial learning
摘 要：order to help investors understand the credit status of target corporations and reduce investment risks,the corporate credit rating model has become an important evaluation tool in the financial *** models are based on statistical learning,machine learning and deep learning especially graph neural networks(GNNs).However,we found that only few models take the hierarchy,heterogeneity or unlabeled data into account in the actual corporate credit rating ***,we propose a novel framework named hierarchical heterogeneous graph neural networks(HHGNN),which can fully model the hierarchy of corporate features and the heterogeneity of relationships between *** addition,we design an adversarial learning block to make full use of the rich unlabeled samples in the financial *** experiments conducted on the public-listed corporate rating dataset prove that HHGNN achieves SOTA compared to the baseline methods.
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