Most　of　existing　correspondence　pruning　methods　only　concentrate　on　gathering　the　context　information　as　much　as　possible　while　neglecting　effective　ways　to　utilize　such　information.　In　order　to　tackle　this　dilemma,　in　this　paper　we　propose　Graph　Context　Transformation　Network　(GCT-Net)　enhancing　context　information　to　conduct　consensus　guidance　for　progressive　correspondence　pruning.　Specifically,　we　design　the　Graph　Context　Enhance　Transformer　which　first　generates　the　graph　network　and　then　transforms　it　into　multi-branch　graph　contexts.　Moreover,　it　employs　self-attention　and　cross-attention　to　magnify　characteristics　of　each　graph　context　for　emphasizing　the　unique　as　well　as　shared　essential　information.　To　further　apply　the　recalibrated　graph　contexts　to　the　global　domain,　we　propose　the　Graph　Context　Guidance　Transformer.　This　module　adopts　a　confident-based　sampling　strategy　to　temporarily　screen　high-confidence　vertices　for　guiding　accurate　classification　by　searching　global　consensus　between　screened　vertices　and　remaining　ones.　The　extensive　experimental　results　on　outlier　removal　and　relative　pose　estimation　clearly　demonstrate　the　superior　performance　of　GCT-Net　compared　to　state-of-the-art　methods　across　outdoor　and　indoor　datasets.