Company financial risk is ubiquitous and early risk assessment for listed
companies can avoid considerable losses. Traditional methods mainly focus on
the financial statements of companies and lack the complex relationships among
them. However, the financial statements are often biased and lagged, making it
difficult to identify risks accurately and timely. To address the challenges,
we redefine the problem as \textbf{company financial risk assessment on
tribe-style graph} by taking each listed company and its shareholders as a
tribe and leveraging financial news to build inter-tribe connections. Such
tribe-style graphs present different patterns to distinguish risky companies
from normal ones. However, most nodes in the tribe-style graph lack attributes,
making it difficult to directly adopt existing graph learning methods (e.g.,
Graph Neural Networks(GNNs)). In this paper, we propose a novel Hierarchical
Graph Neural Network (TH-GNN) for Tribe-style graphs via two levels, with the
first level to encode the structure pattern of the tribes with contrastive
learning, and the second level to diffuse information based on the inter-tribe
relations, achieving effective and efficient risk assessment. Extensive
experiments on the real-world company dataset show that our method achieves
significant improvements on financial risk assessment over previous competing
methods. Also, the extensive ablation studies and visualization comprehensively
show the effectiveness of our method.

研究使用部落式图作为新的财务风险评估方式，提出一种新的基于两个级别的 TH-GNN 模型，包括结构模式编码和信息扩散两个级别，利用对比学习和部落关系分析有效地、高效地识别部落中的风险公司，模型在真实数据集上得到了显著的改进。