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ARXIV:2604.21093 · GNN FRAUD DETECTION · SUBMITTED 24 APR · 20:26 UTC · FRESHNESS STALE
ARXIV:2604.21093GNN FRAUD DETECTIONSUBMITTED 24 APR · 20:26 UTCFRESHNESS STALEBhavana Sajja · arXiv
TravelFraudBench is a configurable GNN evaluation framework for travel fraud ring detection, outperforming baselines and achieving 100% ring recovery with an open-source Python package.
Opportunity summary
Pain TravelFraudBench is a configurable GNN evaluation framework for travel fraud ring detection, outperforming baselines and achieving 100% ring recovery with an open-source Python package.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
TravelFraudBench is a configurable GNN evaluation framework for travel fraud ring detection, outperforming baselines and achieving 100% ring recovery with an open-source Python package. Existing benchmarks--YelpChi, Amazon-Fraud, Elliptic, PaySim--cover single node types or domain-generic…
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…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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. Code…
GNN Fraud Detection moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
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TravelFraudBench is a configurable GNN evaluation framework for travel fraud ring detection, outperforming baselines and achieving 100% ring recovery with an open-source Python package.
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10.48550/arXiv.2604.21093TravelFraudBench is a configurable GNN evaluation framework for travel fraud ring detection, outperforming baselines and achieving 100% ring recovery with an open-source Python package.
Abstract
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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Dimensions overall score 8.0
PROBLEM
TravelFraudBench is a configurable GNN evaluation framework for travel fraud ring detection, outperforming baselines and achieving 100% ring recovery with an open-source Python package. Existing benchmarks--YelpChi, Amazon-Fraud, Elliptic, PaySim--cover single node types or doma...
METHOD
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 m...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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. Code availability is f...
WHY NOW
GNN Fraud Detection moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 20, "author": "Bhavana Sajja", "title": "TRAVELFRAUDBENCH: A Configurable Evaluation Framework for GNN Fraud Ring Detection in Travel Networks", "creation date": null
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TravelFraudBench is a configurable GNN evaluation framework for travel fraud ring detection, outperforming baselines and achieving 100% ring recovery with an open-source Python package.
Segment
GNN Fraud Detection
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