Graph-contrast of learning reimagined, exploring universality. Real-world graphs exhibit diverse structures, including home-affiliacant heterophilic patterns, necessitating the development of a universal graph-contrast of learning GCL framework. Nonetheless, the existing GCLs, especially those with a local focus, lack universality due to the mismatch between the input graph structure and the homophily, assumption for two primary components of GCLs. Firstly, the encoder, commonly graph-comboleution network GCN, operates as a low-pass filter, which assumes the input graph to be home-affiliac. This makes it challenging to aggregate features from neighbor nodes of the same class on, heterophilic graphs. Secondly, the local positive sampling regards neighbor nodes as positive samples. Which is inspired by the homophily assumption. This results in feature similarity amplification for the samples from the different classes i.e. FALSE positive samples. Therefore, it is crucial to feed the encoder and positive sampling of GCLs with home-affiliac graph structures. This paper presents novel GCL framework, named Graph-contrast of exploring universality-rosin, designed to achieve this objective. Specifically, Rosen equips a local graph structure inference module, utilizing the block diagonal property BDP of the affinity matrix extracted from no ego networks. This module can generate the home-affiliac graph structure by selectively removing disassortative veggies. Extensive evaluations validate the effectiveness and universality of Rosen across node, classification and node clustering tasks. Including the Effectiveness Study, Ablation Study, Hyperpermeter Study, and Robustness Study.