Semi-supervised graph convolutional network for Ethereum phishing scam recognition

In recent years, as the blockchain and the cryptocurrency built on it have become popular, a large number of decentralized financial applications have been built on the Ethereum network. This makes the security issues on Ethereum attract more and more researchers' attention. Phishing scams on Ethereum have caused people to suffer huge economic losses. Recently, with the popularity of graph convolutional neural networks (GCN), many models based on GCN for node classification have emerged. However, these current GCN models are difficult to cope with the challenges caused by the lack of side information and labels of nodes in the Ethereum network. In this paper, we propose a semisupervised graph convolutional neural network model based on important neighbors for the identification of phishing scam nodes on Ethereum. In our work, we design the pretext task for the node embedding module so that our model can learn the appropriate node embedding by using a large amount of unlabeled node data. Subsequent experiments show that our proposed model is better than all other baselines, which proves the effectiveness of our model.