Self-supervised　graph　representation　learning　(GRL)　has　shown　great　success　in　scientific　research　and　real-world　applications.　Nevertheless,　one　obstacle　in　GRL　is　the　demand　for　graph　augmentation　(GA),　which　deeply　impacts　the　representation　qualities.　On　the　one　hand,　GA　supplements　the　data　amount　and　enhances　the　robustness　and　quality　of　the　representations.　On　the　other　hand,　collocating　appropriate　augmentations　claims　nontrivial　attempts.　In　this　article,　a　new　method　to　free　GA　is　provided　building　a　novel　fuzzy　view　and　two　crisp　views　of　the　original　graph.　As　all　the　views　are　transformed　from　the　original　graph,　they　are　semantically　similar　and　naturally　considered　to　possess　high-quality　positive　samples.　In　this　way,　the　data　amount　is　compensated　to　a　degree　without　changing　the　raw　node　attributes　or　graph　topology.　Additionally,　to　ensure　the　diversity　of　the　positives,　asymmetric　renormalization　and　noise　perturbation　are　adopted.　Experiments　toward　node-level　tasks　on　several　real-world　datasets　demonstrate　the　competition　against　several　state-of-the-art　models.