Detecting Manipulation in NFT Market Using Graph-based Deep Learning
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Abstract
The rise of non-fungible tokens (NFTs) has increased the risk of fraud and market manipulation. This study introduces a method for detecting wash trading in the NFT marketplace using Graph Neural Networks (GNNs) applied to Ethereum blockchain transaction data. We constructed a heterogeneous graph, used Depth-First Search for labelling, and extracted graph features, including PageRank and degree centrality. We evaluate various classification models: Multilayer Perceptron (MLP), Graph Convolutional Neural Network (GCN), and Heterogeneous Graph Convolutional Neural Network (HeteroGCN). The results show that GNN models, particularly the feature-enhanced HeteroGCN, exhibit superior performance compared to featureless models and traditional tabular baselines. The key contribution of this study is that PageRank and Degree Centrality features significantly improve the accuracy of identifying transactions involved in market manipulation.
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