GraphFAS：面向工业交易网络的自动化图特征生成与选择的分布式系统
GraphFAS: A Distributed System for Automated Graph Feature Generation and Selection in Industrial Transaction Networks
- Ant Group（蚂蚁集团）
- Nanjing University（南京大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
GraphFAS提出基于Boruta的分布式图特征自动生成与选择系统，通过非参数化多跳子图提取和多尺度聚合构建可解释特征，在支付宝部署中实现工程效率数量级提升并优于基线。
AI中文摘要：
工业欺诈检测通常依赖于成本高昂的专家手工特征，这些特征忽视了图结构化的关系信号，而GNN往往无法满足金融风控的可解释性和部署要求。我们提出了GraphFAS（图特征自动选择），一种基于Boruta的分布式特征选择流程，通过以下两点弥合这一差距：（1）一个非参数化的图特征生成模块，通过多跳子图提取和多尺度聚合构建显式、可解释的结构特征，无需学习参数；（2）一种自动化的分布式特征选择算法，扩展了Boruta，采用基于中位数的跨分区聚合，以最少的领域专业知识在规模上稳健地识别信息性特征。与端到端的GNN流水线相比，GraphFAS将特征聚合与模型训练解耦，能够直接集成表格模型，并直接兼容基于TreeSHAP的解释。在支付宝部署后，GraphFAS在工程效率上实现了数量级的提升，同时在大规模图上展现出优于专家驱动和图学习基线的强劲性能。
英文摘要：
Industrial fraud detection often relies on costly expert-crafted features that overlook graph-structured relational signals, while GNNs often do not meet the interpretability and deployment requirements of financial risk control. We propose GraphFAS (Graph Feature Automated Selection), a distributed feature selection procedure based on Boruta that bridges this gap through: (1) a non-parametric graph feature generation module that constructs explicit, interpretable structural features via multi-hop subgraph extraction and multi-scale aggregation without learned parameters; and (2) an automated distributed feature selection algorithm extending Boruta with median-based aggregation across partitions to robustly identify informative features at scale with minimal domain expertise. Compared with end-to-end GNN pipelines, GraphFAS decouples feature aggregation from model training, enabling direct integration with tabular models and direct compatibility with TreeSHAPbased explanations. Deployed in Alipay, GraphFAS delivers orderof-magnitude improvements in engineering efficiency while showing strong performance against expert-driven and graph-learning baselines on large-scale graphs.
