  With the rapid growth of blockchain, an increasing number of users have been
attracted and many implementations have been refreshed in different fields.
Especially in the cryptocurrency investment field, blockchain technology has
shown vigorous vitality. However, along with the rise of online business,
numerous fraudulent activities, e.g., money laundering, bribery, phishing, and
others, emerge as the main threat to trading security. Due to the openness of
Ethereum, researchers can easily access Ethereum transaction records and smart
contracts, which brings unprecedented opportunities for Ethereum scams
detection and analysis. This paper mainly focuses on the Ponzi scheme, a
typical fraud, which has caused large property damage to the users in Ethereum.
By verifying Ponzi contracts to maintain Ethereum's sustainable development, we
model Ponzi scheme identification and detection as a node classification task.
In this paper, we first collect target contracts' transactions to establish
transaction networks and propose a detecting model based on graph convolutional
network (GCN) to precisely distinguishPonzi contracts. Experiments on different
real-world Ethereum datasets demonstrate that our proposed model has promising
results compared with general machine learning methods to detect Ponzi schemes.
