  While transactions with cryptocurrencies such as Ethereum are becoming more
prevalent, fraud and other criminal transactions are not uncommon. Graph
analysis algorithms and machine learning techniques detect suspicious
transactions that lead to phishing in large transaction networks. Many graph
neural network (GNN) models have been proposed to apply deep learning
techniques to graph structures. Although there is research on phishing
detection using GNN models in the Ethereum transaction network, models that
address the scale of the number of vertices and edges and the imbalance of
labels have not yet been studied. In this paper, we compared the model
performance of GNN models on the actual Ethereum transaction network dataset
and phishing reported label data to exhaustively compare and verify which GNN
models and hyperparameters produce the best accuracy. Specifically, we
evaluated the model performance of representative homogeneous GNN models which
consider single-type nodes and edges and heterogeneous GNN models which support
different types of nodes and edges. We showed that heterogeneous models had
better model performance than homogeneous models. In particular, the RGCN model
achieved the best performance in the overall metrics.
