Heterogeneous graphs neural networks based on neighbor relationship filtering

In recent years, heterogeneous graph neural networks have been applied to the analysis of complex networks, and in ethereum transaction, fraudsters disguise themselves as normal transaction accounts through their behavior, increasing the fault tolerance of using heterogeneous graph neural networks. This paper proposes a heterogeneous graph neural network approach based on neighbor filtering to identify fraudulent ethereum accounts. Specifically, firstly, the collected data of all 386612 ethereum transactions are constructed as transaction subsets G1, G2 and G3. Then, to measure the similarity between ethereum transaction accounts, this paper proposes a similarity measure based on random walks and uses reinforcement learning to find the best neighbor for each relation in the heterogeneous network, and aggregation within and between relations to represent the neighborhood relationship between neighboring nodes. To solve the overfitting problem of inter-relationship aggregation, this paper adds initial residuals to inter-relationship aggregation so that the neighbor aggregation process can be applied to deeper GNNs, resulting in a 2% increase in the AUC metric, which is important for the effective identification of ethereum identities.