paper-with-me

Node Classification 벤치마크

Node Classification on Yelp-Fraud

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AUC-ROC

75.7 80.88 86.05 91.23 96.4 2020-08 2026-09 CARE-GNN — 75.7 (2020-08-19) GAT+JK — 90.04 (2021-04-03) RioGNN — 83.54 (2021-04-16) PC-GNN — 79.87 (2021-04-19) RLC-GNN — 85.44 (2021-06-18) BOLT-GRAPH — 93.18 (2023-03-30) SplitGNN — 92.03 (2023-10-21) LEX-GNN — 96.4 (2024-10-21) GTAN — 94.98 (2024-12-24) CARE-GNN — 75.7 (2020-08-19) GAT+JK — 90.04 (2021-04-03) BOLT-GRAPH — 93.18 (2023-03-30) LEX-GNN — 96.4 (2024-10-21)
RankModel AUC-ROC PaperCodeYear
1 LEX-GNN 96.40 LEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud Detection wdhyun/LEX-GNN 2024
2 GTAN 94.98 Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation ai4risk/antifraud · finint/antifraud 2024
3 BOLT-GRAPH 93.18 BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware ThirdAIResearch/BOLT_Benchmarks · thirdairesearch/bolt_benchmarks 2023
4 SplitGNN 92.03 SplitGNN: Spectral Graph Neural Network for Fraud Detection against Heterophily split-gnn/splitgnn · blackboxo/SplitGNN 2023
5 GAT+JK 90.04 New Benchmarks for Learning on Non-Homophilous Graphs CUAI/Non-Homophily-Benchmarks 2021
6 RLC-GNN 85.44 RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection 2021
7 RioGNN 83.54 Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks safe-graph/RioGNN 2021
8 PC-GNN 79.87 Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection PonderLY/PC-GNN 2021
9 CARE-GNN 75.70 Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters dmlc/dgl · safe-graph/DGFraud · YingtongDou/CARE-GNN · +3 2020
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