With　broad　applications　in　network　analysis　and　mining,　Graph　Contrastive　Learning　(GCL)　is　attracting　growing　research　interest.　Despite　its　successful　usage　in　extracting　concise　but　useful　information　through　contrasting　different　augmented　graph　views　as　an　outstanding　self-supervised　technique,　GCL　is　facing　a　major　challenge　in　how　to　make　the　semantic　information　extracted　well-organized　in　structure　and　consequently　easily　understood　by　a　downstream　classifier.　In　this　paper,　we　propose　a　novel　cluster-based　GCL　framework　to　obtain　a　semantically　well-formed　structure　of　node　embeddings　via　maximizing　mutual　information　between　input　graph　and　output　embeddings,　which　also　provides　a　more　clear　decision　boundary　through　accomplishing　a　cluster-level　global-local　contrastive　task.　We　further　argue　in　theory　that　the　proposed　method　can　correctly　maximize　the　mutual　information　between　an　input　graph　and　output　embeddings.　Moreover,　we　further　improve　the　proposed　method　for　better　practical　performance　by　incorporating　additional　refined　gadgets,　e.g.,　measuring　uncertainty　of　clustering　and　additional　structural　information　extraction　via　local-local　node-level　contrasting　module　enhanced　by　Graph　Cut.　Lastly,　extensive　experiments　are　carried　out　to　demonstrate　the　practical　performance　gain　of　our　method　in　six　real-world　datasets　over　the　most　prevalent　existing　state-of-the-art　models.