AutoAudit：挖掘会计和时间演化图
AutoAudit: Mining Accounting and Time-Evolving Graphs
摘要：
我们如何在大规模的图形会计数据集中发现洗钱？如何在时间演化的会计图表中识别最可疑的时期？在时间限制下，从业者应该优先考虑哪些类型的帐户和事件？为了解决会计和审计任务中的这些关键挑战，我们提出了一个灵活的系统，称为AutoAudit，这对审计师和风险管理专业人员来说是有价值的。综上所述，所提出的系统有四个主要优点：（a）“Smurfing”检测，在真实世界的数据集中自动发现近100%的注入洗钱交易。(b)Attention Routing，关注时间演化图中最可疑的部分，并提供直观的解释。(c)洞察发现，确定类似的月对模式证明了“成功的故事”和模式遵循幂律对数逻辑尺度。(d)可扩展性和通用性，确保AutoAudit线性扩展，并可以轻松扩展到其他真实世界的图形数据集。在各种真实数据集上的实验表明了该方法的有效性。为了促进可重复性和可访问性，我们在https://github.com/mengchillee/AutoAudit上公开了代码、图和结果。
英文摘要：
How can we spot money laundering in large-scale graph-like accounting datasets? How to identify the most suspicious period in a time-evolving accounting graph? What kind of accounts and events should practitioners prioritize under time constraints? To tackle these crucial challenges in accounting and auditing tasks, we propose a flexible system called AutoAudit, which can be valuable for auditors and risk management professionals. To sum up, there are four major advantages of the proposed system: (a) "Smurfing" Detection, spots nearly 100% of injected money laundering transactions automatically in real-world datasets. (b) Attention Routing, attends to the most suspicious part of time-evolving graphs and provides an intuitive interpretation. (c) Insight Discovery, identifies similar month-pair patterns proved by "success stories" and patterns following Power Laws in log-logistic scales. (d) Scalability and Generality, ensures AutoAudit scales linearly and can be easily extended to other real-world graph datasets. Experiments on various real-world datasets illustrate the effectiveness of our method. To facilitate reproducibility and accessibility, we make the code, figure, and results public at https://github.com/mengchillee/AutoAudit.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
