With　the　rise　of　contrastive　learning,　unsupervised　graph　representation　learning　has　shown　strong　competitiveness.　However,　existing　graph　contrastive　models　typically　either　focus　on　the　local　view　of　graphs　or　take　simple　considerations　of　both　global　and　local　views.　This　may　cause　these　models　to　overemphasize　the　importance　of　individual　nodes　and　their　ego-networks,　or　to　result　in　poor　learning　of　global　knowledge　and　affect　the　learning　of　local　views.　Additionally,　most　graph　representation　learning　models　pay　attention　to　topological　proximity,　assuming　that　nodes　that　are　closer　in　graph　topology　are　more　similar.　However,　in　real　world,　close　nodes　may　be　dissimilar,　which　makes　the　learned　embeddings　incorporate　inappropriate　messages　and　thus　lack　discrimination.　To　address　these　issues,　we　propose　a　novel　unsupervised　graph　representation　learning　model　by　contrasting　cluster　assignments,　called　GRCCA.　To　comprehensively　explore　the　global　and　local　views,　it　combines　multi-view　contrastive　learning　and　clustering　algorithms　with　an　opposite　augmentation　strategy.　It　leverages　clustering　algorithms　to　capture　fine-grained　global　information　and　explore　potential　relevance　between　nodes　in　different　augmented　perspectives,　while　preserving　high-quality　global　and　local　information　through　contrast　between　nodes　and　prototypes.　The　opposite　augmentation　strategy　further　enhances　the　contrast　of　both　views,　allowing　the　model　to　excavate　more　invariant　features.　Experimental　results　show　that　GRCCA　has　strong　competitiveness　compared　to　state-of-the-art　models　in　different　graph　analysis　tasks.　IEEE