An　essential　challenge　in　graph　data　analysis　and　mining　is　to　simply　and　effectively　deal　with　large-scale　network　data　that　is　expanding　dynamically.　Although　batch-based　parallel　graph　computation　frameworks　have　better　accuracy,　they　cannot　process　incremental　data　on　time　and　need　to　be　recomputed.　To　process　real-time　data,　stream　processing　applications　need　to　be　redeveloped,　which　increases　the　redundancy　of　work,　and　some　existing　dynamic　graph　computation　schemes　are　not　generalizable.　This　paper　proposes　a　unified　stream　and　batch　graph　computing　model(USBGM).　The　model　is　compatible　with　both　stream　and　batch　graph　computing.　Graph　operators　and　algorithms　developed　based　on　the　model　can　handle　stream　and　batch　graph　data　in　a　unified　manner.　The　experiments　on　real-world　and　artificial　networks　verified　the　effectiveness　and　efficiency　of　the　model.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.