  Blockchain has widespread applications in the financial field but has also
attracted increasing cybercrimes. Recently, phishing fraud has emerged as a
major threat to blockchain security, calling for the development of effective
regulatory strategies. Nowadays network science has been widely used in
modeling Ethereum transaction data, further introducing the network
representation learning technology to analyze the transaction patterns. In this
paper, we consider phishing detection as a graph classification task and
propose an end-to-end Phishing Detection Graph Neural Network framework
(PDGNN). Specifically, we first construct a lightweight Ethereum transaction
network and extract transaction subgraphs of collected phishing accounts. Then
we propose an end-to-end detection model based on Chebyshev-GCN to precisely
distinguish between normal and phishing accounts. Extensive experiments on five
Ethereum datasets demonstrate that our PDGNN significantly outperforms general
phishing detection methods and scales well in large transaction networks.
