首页|Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks
Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
In 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 market.These models are based on statistical learning,ma-chine 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 process.Therefore,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 corporations.In addition,we design an adversarial learning block to make full use of the rich un-labeled samples in the financial data.Extensive experiments conducted on the public-listed corporate rating dataset prove that HHGNN achieves SOTA compared to the baseline methods.
Center for Research on Intelligent Perception and Computing,National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
NETL
NSTL
万方数据
