Neighborhood Subgraph-Based Illicit Transaction Detection in Cryptocurrency Networks

Abstract

With the rise of cryptocurrencies, illicit activities such as money laundering, fraud, and Ponzi schemes have gained attention. Traditional methods using graph neural networks (GNNs) to detect illicit transactions treat the entire transaction network as input, which works well on small networks but struggles with large-scale blockchain data. To address this limitation, the authors propose a neighborhood subgraph-based method that combines GCN and LSTM. The GCN captures information from neighboring nodes for each transaction, enhancing the understanding of the network structure, while the LSTM tracks the sequence and variations of fund flows. Experimental results show that by using 3-hop neighborhood subgraphs, the method outperforms other baseline models while requiring data from only an average of 80 nodes, thereby significantly improving efficiency compared to methods that process the entire transaction network.