Survey for Exploring Blockchain with Graph Neural Network

This paper explores the use of GNN architectures, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and GraphSAGE, in modeling blockchain data. A specific focus is given to their applications in cryptocurrency transaction networks, where GNNs can detect fraudulent activities and track illicit fund flows by learning from transaction patterns and network structure. Additionally, future directions for GNN research in blockchain are discussed, including scalability challenges, privacy-preserving models, and multi-chain graph analysis. Through these advancements, GNNs offer significant potential for enhancing the security, transparency, and efficiency of blockchain and cryptocurrency ecosystems. This paper also shows an example of fraud detection for Ethereum cryptocurrencies.