Inspired　by　the　huge　powerful　representation　ability　of　graph　neural　networks　(GNNs),　GNNs　begin　to　solve　node　classification　in　cross　networks.　However,　current　GNNs　are　designed　for　single　graph,　and　overlook　the　distribution　shift　in　cross　network　which　degrades　the　performance　on　test　nodes.　In　this　paper,　we　firstly　analyze　deep　cross-network　embeddings　based　on　GNNs　and　illustrate　that　in　cross-network　node　classification　training　and　test　embedding　spaces　have　different　distributions.　According　to　domain　adaptation　and　our　analysis,　we　propose　a　novel　framework　and　loss　function,　which　introduces　optimal　transport　to　GNNs　and　generates　transferable　embeddings.　Extensive　experiments　in　citation　networks　verify　the　effectiveness　of　our　method　OT-DCNE.　©　2022　The　Author(s).