Detecting　fraudulent　users　in　social　networks　could　reduce　online　fraud　and　telecommunication　fraud　cases,　which　is　essential　to　protect　the　lives　and　properties　of　internet　users　and　maintain　social　harmony　and　stability.　We　study　how　to　detect　fraudulent　users　by　using　heterogeneous　graph　representation　learning　and　propose　a　heterogeneous　graph　representation　learning　algorithm　to　learn　user　node　embeddings　to　reduce　human　intervention.　The　experimental　results　show　promising　results.　This　article　investigates　how　to　use　better　heterogeneous　graph　representation　learning　to　detect　fraudulent　users　in　social　networks　and　improve　detection　accuracy.