The　session-based　recommendation　(SBR)　is　a　task　to　predict　user　next　action　based　on　anonymous　behavior　sequences.　Most　existing　studies　utilize　global　context　in　conjunction　with　current　session　information　to　improve　recommendation　results.　However,　the　local　and　global　perspectives　have　an　inherent　conflict,　and　it　has　been　a　great　challenge　to　integrate　the　two　types　of　information.　In　this　paper,　we　propose　a　novel　recommendation　method,　namely,　dual-grained　global　graph　neural　network　(DG-GNN),　to　capture　collaborative　information　from　all　sessions　in　a　more　subtle　manner　to　model　current　user　preferences.　Specifically,　we　convert　the　local　and　global　content　into　two　graphs　and　process　them　with　the　corresponding　encoders.　The　local　section　exploits　the　graph　attention　network　to　learn　a　set　of　item-level　embeddings.　The　global　section　collects　the　pairwise　item　transitions　relevant　to　the　current　session　for　constructing　graphs　and　exploits　a　lightweight　encoder　to　learn　a　single　session-level　embedding,　which　reduces　the　influence　of　irrelevant　information　from　global　content　on　the　local　section.　Moreover,　a　new　position　embedding　mechanism　is　proposed　to　generate　different　position　embeddings　depending　on　the　length　of　sessions　for　enhancing　the　ability　to　capture　position　information.　Experiments　on　real-world　datasets　demonstrate　that　DG-GNN　outperforms　other　state-of-the-art　methods.　©　2023　SPIE.