Quantum-Enhanced Graph Analytics: A Hybrid AI Framework for Seller Fraud Detection in Online Marketplaces

Abstract

Fraudulent seller networks in e-commerce platforms exploit relational patterns across buyers, products, and transactions to perpetrate large-scale scams that evade traditional detection systems. Graph Neural Networks (GNNs) provide end-to-end representation learning on graph structures, enabling detection of anomalous subgraphs indicative of fraud rings. Complementing GNNs, TinyML brings on-device inference for continuous, low-latency edge monitoring, and emerging Quantum Neural Networks (QNNs) promise enriched feature spaces for small-data regimes. This article delivers an expanded, scholarly framework covering: (1) formalization of fraudulent seller detection as a graph anomaly-ranking problem; (2) data pipelines and graph construction best practices; (3) detailed GNN architectures (GCN, GAT, GraphSAGE, graph autoencoders) and hybrid classifiers; (4) integration of TinyML for edge deployments; (5) incorporation of QNN modules for anomaly scoring; (6) comprehensive experimental evaluation on real and synthetic datasets; and (7) ethical, security, and regulatory considerations. We conclude with a multi-horizon research roadmap from near-term pilots to long-term fault-tolerant quantum defenses.