Over　the　past　few　years,　graph　contrastive　learning　(GCL)　has　gained　great　success　in　processing　unlabeled　graph-structured　data,　but　most　of　the　existing　GCL　methods　are　based　on　instance　discrimination　task　which　typically　learns　representations　by　minimizing　the　distance　between　two　versions　of　the　same　instance.　However,　different　from　images,　which　are　assumed　to　be　independently　and　identically　distributed,　graphs　present　relational　information　among　data　instances,　in　which　each　instance　is　related　to　others　by　links.　Furthermore,　the　relations　are　heterogeneous　in　many　cases.　The　instance　discrimination　task　cannot　make　full　use　of　the　relational　information　inherent　in　the　graph-structured　data.　To　solve　the　above-mentioned　problems,　this　paper　proposes　a　relation-aware　graph　contrastive　learning　method,　called　RGCL.　Aiming　to　capture　the　most　important　heterogeneous　relations　in　the　graph,　RGCL　explicitly　models　the　edges,　and　then　pulls　semantically　similar　pairs　of　edges　together　and　pushes　dissimilar　ones　apart　with　contrastive　regularization.　By　exploiting　the　full　potential　of　the　relationship　among　nodes,　RGCL　overcomes　the　limitations　of　previous　GCL　methods　based　on　instance　discrimination.　The　experimental　results　demonstrate　that　the　proposed　method　outperforms　a　series　of　graph　contrastive　learning　frameworks　on　widely　used　benchmarks,　which　justifies　the　effectiveness　of　our　work.