  With the development of Web 3.0 which emphasizes decentralization, blockchain
technology ushers in its revolution and also brings numerous challenges,
particularly in the field of cryptocurrency. Recently, a large number of
criminal behaviors continuously emerge on blockchain, such as Ponzi schemes and
phishing scams, which severely endanger decentralized finance. Existing
graph-based abnormal behavior detection methods on blockchain usually focus on
constructing homogeneous transaction graphs without distinguishing the
heterogeneity of nodes and edges, resulting in partial loss of transaction
pattern information. Although existing heterogeneous modeling methods can
depict richer information through metapaths, the extracted metapaths generally
neglect temporal dependencies between entities and do not reflect real
behavior. In this paper, we introduce Time-aware Metapath Feature Augmentation
(TMFAug) as a plug-and-play module to capture the real metapath-based
transaction patterns during Ponzi scheme detection on Ethereum. The proposed
module can be adaptively combined with existing graph-based Ponzi detection
methods. Extensive experimental results show that our TMFAug can help existing
Ponzi detection methods achieve significant performance improvements on the
Ethereum dataset, indicating the effectiveness of heterogeneous temporal
information for Ponzi scheme detection.
