自动化生成可解释的图特征，提升金融反欺诈效率
GraphFAS: A Distributed System for Automated Graph Feature Generation and Selection in Industrial Transaction Networks
- 用多跳子图提取+多尺度聚合生成无参数图特征
- 分布式硼特算法筛选出高信息量特征，精度优于基线
- 适合需要可解释性与快速部署的工业风控场景
工业反欺诈常依赖昂贵的人工特征设计，忽视图结构关系信号；而图神经网络难以满足金融风控对可解释性和部署的要求。我们提出GraphFAS（图特征自动选择），一种基于Boruta的分布式特征选择系统：(1) 无参数的图特征生成模块，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；(2) 扩展Boruta的分布式特征选择算法，采用中位数聚合跨分区结果，在大规模数据上鲁棒识别有效特征，仅需少量领域知识。相比端到端GNN流程，GraphFAS将特征聚合与模型训练解耦，可直接接入表格模型并兼容TreeSHAP解释。在支付宝部署后，工程效率提升数量级，并在大规模图上性能超越专家设计与图学习基线。
原文摘要 · Abstract (English)
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.
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