Graph　anomaly　detection　(GAD)　has　been　extensively　studied　in　recent　years.　GAD　aims　to　detect　nodes,　edges,　and　subgraphs　that　exhibit　characteristics　and　distributions　different　from　those　of　the　majority　of　graph　data.　With　the　advancement　of　deep　learning,　many　researchers　have　applied　machine　learning　to　address　anomaly　detection　at　various　scales.　In　this　paper,　we　classify　GAD　methods　into　detector-based　and　classifier-based　approaches　and　provide　a　brief　introduction　and　summary　of　relevant　articles　from　the　past　three　years.　Finally,　we　analyze　the　challenges　and　future　development　directions　in　the　field　of　GAD.　©　2024　The　Authors.