发表机构
RepRisk AG; The University of Tokyo; The Canon Institute for Global Studies（睿略公司; 东京大学; 佳能全球研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对业务行为风险监测的数据可见性偏差问题，提出可见性与关系感知的GCNII框架，通过企业所有权图的正-未标记节点分类，提升了对低可见性企业未来行为相关事件的预测排序性能。
AI 中文摘要
业务行为风险的监测受到稀疏、不均衡且存在可见性偏差的数据阻碍。现有研究表明，业务行为风险信息与媒体报道会通过供应链、同行及企业结构网络传播，但许多企业的事件记录仍不完整。因此，未报告事件的情况可能反映的是覆盖范围有限，而非潜在业务行为风险不存在。本文研究企业间关系能否提升对未来已记录的行为相关事件的预测，尤其是针对先前可见性有限的企业。我们将该任务建模为企业所有权图上的正-未标记节点分类，其中有记录事件的企业被视为已标记正例，无记录事件的企业则为未标记样本。随后，我们提出一种可见性与关系感知的GCNII框架，该框架结合特定关系的消息传递与非负正-未标记学习，以处理未标记集中的正例污染问题。在前瞻性评估中，与非图方法及简单图方法基准相比，所提方法实现了观测到的最强排序性能。结果进一步表明，基于图的推理在无先前记录事件的企业中仍保留其预测价值。这些发现证明，企业间关系结构作为补充信息源，对扩展风险优先级排序具有重要价值。
英文摘要
The monitoring of business conduct risk is hindered by sparse, uneven, and visibility-biased data. Prior studies show that business conduct risk information and media coverage propagate through supply chain, peer, and corporate structure networks, yet incident records remain incomplete for many firms. As a result, the absence of reported events could reflect limited coverage rather than the absence of underlying business conduct risk. This paper examines whether inter-firm relationships can improve the prediction of future recorded conduct related incidents, particularly among firms with limited prior visibility. We formulate the task as Positive--Unlabeled node classification on a corporate ownership graph, where firms with recorded incidents are treated as labeled positives and firms without recorded incidents remain unlabeled. We then propose a visibility- and relation-aware GCNII framework that combines relation specific message passing with non-negative Positive--Unlabeled learning to account for positive contamination in the unlabeled set. In a forward-looking evaluation, the proposed approach achieved the strongest observed ranking performance relative to non-graph- and simple graph-based benchmarks. The results further show that graph-based inference retains its predictive value among firms without prior recorded incidents. These findings demonstrate the value of inter-firm relational structure as a complementary source of information for extending risk prioritization
