# Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks

arXiv: https://arxiv.org/abs/2301.13492 · HTML: https://mlsanity.com/paper/2301.13492/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 31 Jan 2023.

## Builds on (most cited first, 19 of 19)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Simple and Deep Graph Convolutional Networks](https://mlsanity.com/paper/2007.02133/) (2020)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training](https://mlsanity.com/paper/2006.09963/) (2020)
- [Towards Deeper Graph Neural Networks](https://mlsanity.com/paper/2007.09296/) (2020)
- [Adaptive Sampling Towards Fast Graph Representation Learning](https://mlsanity.com/paper/1809.05343/) (2018)
- [Graph Wavelet Neural Network](https://mlsanity.com/paper/1904.07785/) (2019)
- [Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning](https://mlsanity.com/paper/2007.16002/) (2020)
- [Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification](https://mlsanity.com/paper/2007.13435/) (2020)
- [Form 10-Q Itemization](https://mlsanity.com/paper/2104.11783/) (2021)
- [Neuron Campaign for Initialization Guided by Information Bottleneck Theory](https://mlsanity.com/paper/2108.06530/) (2021)
- [Every Corporation Owns Its Structure: Corporate Credit Ratings via Graph Neural Networks](https://mlsanity.com/paper/2012.01933/) (2020)
- [Neuron with Steady Response Leads to Better Generalization](https://mlsanity.com/paper/2111.15414/) (2021)
- [Visual analytics for networked-guarantee loans risk management](https://mlsanity.com/paper/1705.02937/) (2017)

## Built on by (most cited first, 10 of 10)

- [Homophily-oriented Heterogeneous Graph Rewiring](https://mlsanity.com/paper/2302.06299/) (2023)
- [Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network](https://mlsanity.com/paper/2302.00873/) (2023)
- [Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer](https://mlsanity.com/paper/2308.09499/) (2023)
- [On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering](https://mlsanity.com/paper/2306.03624/) (2023)
- [Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation](https://mlsanity.com/paper/2305.05827/) (2023)
- [Enhance GNNs with Reliable Confidence Estimation via Adversarial Calibration Learning](https://mlsanity.com/paper/2503.18235/) (2025)
- [HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessment](https://mlsanity.com/paper/2311.04244/) (2023)
- [Graph Dimension Attention Networks for Enterprise Credit Assessment](https://mlsanity.com/paper/2407.11615/) (2024)
- [Corporate Fraud Detection in Rich-yet-Noisy Financial Graph](https://mlsanity.com/paper/2502.19305/) (2025)
- [DefaultGNN: A Dual-Perspective GNN Framework for Predicting Corporate Default from Buyer-Seller Transaction Networks](https://mlsanity.com/paper/2609.25542/) (2026)

## Abstract (arXiv)

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 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.