Ponzi　schemes　remain　a　significant　challenge　to　financial　security,　particularly　on　blockchain　platforms　such　as　Ethereum,　where　the　autonomy　of　smart　contracts　facilitates　fraudulent　activities.　Existing　detection　methods,　typically　framed　as　binary　classification　tasks,　often　face　the　challenge　of　extreme　class　imbalance,　while　conventional　graph-based　detection　methods　fail　to　capture　asymmetric　transaction　dynamics.　To　address　these　limitations,　we　introduce　Directed　Graph　Neural　Networks　for　Anomaly　Detection　of　Smart　Ponzi　Schemes　(DGAD-SPS),　a　novel　approach　that　leverages　directed　graph　analysis　to　detect　Ponzi　schemes　in　Ethereum＇s　transactional　data.　By　formulating　the　problem　as　an　anomaly　detection　task　on　a　directed　graph,　DGAD-SPS　captures　the　asymmetrical　and　directional　nature　of　Ethereum　transactions,　enabling　a　more　accurate　differentiation　between　fraudulent　and　legitimate　contracts.　The　proposed　model　employs　a　self-supervised　learning　paradigm　that　combines　contrastive　and　generative　learning　to　derive　node　embeddings　without　relying　on　labeled　data,　making　it　particularly　well-suited　for　imbalanced　datasets.　Experimental　validation　confirms　DGAD-SPS＇s　effectiveness　in　real-world　Ponzi　scheme　detection　through　explicit　modeling　of　directional　transaction　relationships　and　robust　performance　under　severe　data　imbalance　conditions.