  A smart Ponzi scheme is a new form of economic crime that uses Ethereum smart
contract account and cryptocurrency to implement Ponzi scheme. The smart Ponzi
scheme has harmed the interests of many investors, but researches on smart
Ponzi scheme detection is still very limited. The existing smart Ponzi scheme
detection methods have the problems of requiring many human resources in
feature engineering and poor model portability. To solve these problems, we
propose a data-driven smart Ponzi scheme detection system in this paper. The
system uses dynamic graph embedding technology to automatically learn the
representation of an account based on multi-source and multi-modal data related
to account transactions. Compared with traditional methods, the proposed system
requires very limited human-computer interaction. To the best of our knowledge,
this is the first work to implement smart Ponzi scheme detection through
dynamic graph embedding. Experimental results show that this method is
significantly better than the existing smart Ponzi scheme detection methods.
