构建可配置的旅行平台欺诈团伙检测基准，验证图神经网络在复杂结构中的表现。
TRAVELFRAUDBENCH: A Configurable Evaluation Framework for GNN Fraud Ring Detection in Travel Networks
- 设计三种旅行领域特有欺诈拓扑，支持节点与边类型灵活配置
- GraphSAGE在欺诈识别中达到0.992 AUC，环路回收率100%
- 开源框架含预生成数据集，支持多种图学习库和负责任AI标注
我们提出TravelFraudBench（TFG），一个用于评估图神经网络（GNN）在旅行平台图中欺诈团伙检测性能的可配置基准。现有基准（YelpChi、Amazon-Fraud、Elliptic、PaySim）仅覆盖单一节点类型或通用模式，缺乏对结构性差异欺诈环路的评估能力。TFG在包含9种节点类型和12种边类型的异质图中，模拟三种旅行特定环路：票务欺诈（共享设备/IP簇的星形拓扑）、幽灵酒店计划（评论者与酒店的二分完全团）以及账户接管环（忠诚度转移链）。环路规模、数量、欺诈率、图大小（500至200,000节点）及组成均可调节。采用环路级划分策略，确保每个环完整存在于单一分区，避免归纳标签泄露。评估六种方法：MLP、GraphSAGE、RGCN-proj、HAN、RGCN和PC-GNN。GraphSAGE取得0.992 AUC，RGCN-proj为0.987，分别优于MLP基线（0.938）5.5和5.0个百分点，证明图结构具有显著判别力。HAN表现与MLP相当（AUC=0.935）。在环路恢复任务中（至少80%成员被同时标记），GraphSAGE实现全类型100%恢复，而MLP仅为17–88%。边类型消融实验表明设备与IP共现是主要信号：移除uses_device导致AUC下降5.2个百分点。TFG以MIT许可证开源，提供PyG、DGL、NetworkX导出器及预生成数据集（https://huggingface.co/datasets/bsajja7/travel-fraud-graphs），并附带Croissant元数据与负责任AI字段。
原文摘要 · Abstract (English)
We introduce TravelFraudBench (TFG), a configurable benchmark for evaluating graph neural networks (GNNs) on fraud ring detection in travel platform graphs. Existing benchmarks--YelpChi, Amazon-Fraud, Elliptic, PaySim--cover single node types or domain-generic patterns with no mechanism to evaluate across structurally distinct fraud ring topologies. TFG simulates three travel-specific ring types--ticketing fraud (star topology with shared device/IP clusters), ghost hotel schemes (reviewer x hotel bipartite cliques), and account takeover rings (loyalty transfer chains)--in a heterogeneous graph with 9 node types and 12 edge types. Ring size, count, fraud rate, scale (500 to 200,000 nodes), and composition are fully configurable. We evaluate six methods--MLP, GraphSAGE, RGCN-proj, HAN, RGCN, and PC-GNN--under a ring-based split where each ring appears entirely in one partition, eliminating transductive label leakage. GraphSAGE achieves AUC=0.992 and RGCN-proj AUC=0.987, outperforming the MLP baseline (AUC=0.938) by 5.5 and 5.0 pp, confirming graph structure adds substantial discriminative power. HAN (AUC=0.935) is a negative result, matching the MLP baseline. On the ring recovery task (>=80% of ring members flagged simultaneously), GraphSAGE achieves 100% recovery across all ring types; MLP recovers only 17-88%. The edge-type ablation shows device and IP co-occurrence are the primary signals: removing uses_device drops AUC by 5.2 pp. TFG is released as an open-source Python package (MIT license) with PyG, DGL, and NetworkX exporters and pre-generated datasets at https://huggingface.co/datasets/bsajja7/travel-fraud-graphs, with Croissant metadata including Responsible AI fields.
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