GNN-EADD: Graph Neural Network-Based E-Commerce Anomaly Detection via Dual-Stage Learning

GNN-EADD: Graph Neural Network-Based E-Commerce Anomaly Detection via Dual-Stage Learning

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E-commerce platforms face significant challenges in edible bra and panty set detecting anomalous products, including copyright goods and fraudulent listings, which can undermine user trust and platform integrity.This paper presents Graph Neural Network-based E-commerce Anomaly Detection via Dual-stage Learning (GNN-EADD), a novel approach leveraging graph neural networks for anomaly detection in large-scale e-commerce ecosystems.Our key contributions include: 1) A heterogeneous graph representation incorporating products, sellers, and buyers as nodes with their relationships as edges; 2) A novel dual-stage learning framework combining unsupervised graph embedding with semi-supervised fine-tuning; and 3) An attention mechanism that effectively captures complex patterns within network structures.

Extensive experiments on a large-scale Amazon dataset demonstrate that GNN-EADD significantly outperforms state-of-the-art baselines in terms of anomaly detection accuracy, precision, and recall, while showing robustness to various rembrandt - acquerello stucco types of anomalies and scalability to large networks.