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GraphFAS: A Distributed System for Automated Graph Feature Generation and Selection in Industrial Transaction Networks

arXiv 2609.08970 ↗First posted 8 Sep 2026cs.LG cs.AI cs.DCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 34 references that are in the map. First posted on arXiv 8 Sep 2026.

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18 of 34 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  5. 2019Simplifying Graph Convolutional Networkscited by 1,470
  6. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  7. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  8. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  9. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  10. 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 279
  11. 2022Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPscited by 42
  12. 2025Turning Tabular Foundation Models into Graph Foundation Modelscited by 30
6 more
  1. 2025GraphPFN: A Prior-Data Fitted Graph Foundation Modelcited by 20
  2. 2025Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Modelscited by 12
  3. 2025Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?cited by 8
  4. 2024GraphRPM: Risk Pattern Mining on Industrial Large Attributed Graphscited by 1
  5. 2025Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphscited by 1
  6. 2025GraphGen+: Advancing Distributed Subgraph Generation and Graph Learning On Industrial Graphscited by 1

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