Graph　representation　learning　has　recently　garnered　significant　attention　due　to　its　wide　applications　in　graph　analysis　tasks.　It　is　well-known　that　real　-world　networks　are　dynamic,　with　edges　and　nodes　evolving　over　time.　This　presents　unique　challenges　that　are　distinct　from　those　of　static　networks.　However,　most　graph　representation　learning　methods　are　either　designed　for　static　graphs,　or　address　only　partial　challenges　associated　with　dynamic　graphs.　They　overlook　the　intricate　interplay　between　topology　and　temporality　in　the　evolution　of　dynamic　graphs　and　the　complexity　of　sequence　modeling.　Therefore,　we　propose　a　new　dynamic　graph　representation　learning　model,　called　as　R-GraphSAGE,　which　takes　comprehensive　considerations　for　embedding　dynamic　graphs.　By　incorporating　a　recurrent　structure　into　GraphSAGE,　the　proposed　RGraphSAGE　explores　structural　and　temporal　patterns　integrally　to　capture　more　fine-grained　evolving　patterns　of　dynamic　graphs.　Additionally,　it　offers　a　lightweight　architecture　to　decrease　the　computational　costs　for　handling　snapshot　sequences,　achieving　a　balance　between　performance　and　complexity.　Moreover,　it　can　inductively　process　the　addition　of　new　nodes　and　adapt　to　the　situations　without　labels　and　node　attributes.　The　performance　of　the　proposed　R-GraphSAGE　is　evaluated　across　various　downstream　tasks　with　both　synthetic　and　real　-world　networks.　The　experimental　results　demonstrate　that　it　outperforms　state-of-the-art　baselines　by　a　significant　margin　in　most　cases.