Node Classification On Yelpchi

Metrics

AUC-ROC

Results

Performance results of various models on this benchmark

		Paper Title
LEX-GNN	96.40	LEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud Detection
GTAN	94.98	Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation
BOLT-GRAPH	93.18	BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware
SplitGNN	92.03	SplitGNN: Spectral Graph Neural Network for Fraud Detection against Heterophily
GAT+JK	90.04	New Benchmarks for Learning on Non-Homophilous Graphs
RLC-GNN	85.44	RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection
RioGNN	83.54	Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
PC-GNN	79.87	Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection
CARE-GNN	75.70	Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

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Node Classification On Yelpchi | SOTA | HyperAI