  Blockchain technology has the characteristics of decentralization,
traceability and tamper-proof, which creates a reliable decentralized trust
mechanism, further accelerating the development of blockchain finance. However,
the anonymization of blockchain hinders market regulation, resulting in
increasing illegal activities such as money laundering, gambling and phishing
fraud on blockchain financial platforms. Thus, financial security has become a
top priority in the blockchain ecosystem, calling for effective market
regulation. In this paper, we consider identifying Ethereum accounts from a
graph classification perspective, and propose an end-to-end graph neural
network framework named Ethident, to characterize the behavior patterns of
accounts and further achieve account de-anonymization. Specifically, we first
construct an Account Interaction Graph (AIG) using raw Ethereum data. Then we
design a hierarchical graph attention encoder named HGATE as the backbone of
our framework, which can effectively characterize the node-level account
features and subgraph-level behavior patterns. For alleviating account label
scarcity, we further introduce contrastive self-supervision mechanism as
regularization to jointly train our framework. Comprehensive experiments on
Ethereum datasets demonstrate that our framework achieves superior performance
in account identification, yielding 1.13% ~ 4.93% relative improvement over
previous state-of-the-art. Furthermore, detailed analyses illustrate the
effectiveness of Ethident in identifying and understanding the behavior of
known participants in Ethereum (e.g. exchanges, miners, etc.), as well as that
of the lawbreakers (e.g. phishing scammers, hackers, etc.), which may aid in
risk assessment and market regulation.
