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Blockchain technology revolutionizes the Internet, but also poses increasingrisks, particularly in cryptocurrency finance. On the Ethereum platform, Ponzischemes, phishing scams, and a variety of other frauds emerge. Existing Ponzischeme detection approaches based on heterogeneous transaction graph modelingleverages semantic information between node (account) pairs to establishconnections, overlooking the semantic attributes inherent to the edges(interactions). To overcome this, we construct heterogeneous Ethereuminteraction graphs with multiple triplet interaction patterns to better depictthe real Ethereum environment. Based on this, we design a new framework namedmulti-triplet augmented heterogeneous graph neural network (MAHGNN) for Ponzischeme detection. We introduce the Conditional Variational Auto Encoder (CVAE)to capture the semantic information of different triplet interaction patterns,which facilitates the characterization on account features. Extensiveexperiments demonstrate that MAHGNN is capable of addressing the problem ofmulti-edge interactions in heterogeneous Ethereum interaction graphs andachieving state-of-the-art performance in Ponzi scheme detection.

Multi-triplet Feature Augmentation for Ponzi Scheme Detection in Ethereum