Burgeoning　graph　contrastive　learning　(GCL)　stands　out　in　the　graph　domain　with　low　annotated　costs　and　high　model　performance　improvements,　which　is　typically　composed　of　three　standard　configurations:　1)　graph　data　augmentation　(GraphDA),　2)　multi-branch　graph　neural　network　(GNN)　encoders　and　projection　heads,　3)　and　contrastive　loss.　Unfortunately,　the　diverse　GraphDA　may　corrupt　graph　semantics　to　different　extents　and　meanwhile　greatly　burdens　the　time　complexity　on　hyperparameter　search.　Besides,　the　multi-branch　contrastive　framework　also　demands　considerable　training　consumption　on　encoding　and　projecting.　In　this　paper,　we　propose　one　simplified　GCL　model　to　simultaneously　address　these　problems　via　the　minimal　components　of　a　general　graph　contrastive　framework,　i.e.,　a　GNN　encoder　and　a　projection　head.　The　proposed　model　treats　the　node　representations　generated　by　the　GNN　encoder　and　the　projection　head　as　positive　pairs　while　considering　all　other　representations　as　negatives,　which　not　only　liberates　the　model　from　the　dependency　on　GraphDA　but　also　streamlines　the　traditional　multi-branch　contrastive　learning　framework　into　a　more　efficient　single-streamlined　one.　Through　the　in-depth　theoretical　analysis　on　the　objective　function,　the　mystery　of　why　the　proposed　model　works　is　illustrated.　Empirical　experiments　on　multiple　public　datasets　demonstrate　that　the　proposed　model　still　ensures　performance　to　be　comparative　with　current　advanced　self-supervised　GNNs.　©　1989-2012　IEEE.