Digitized　pathological　diagnosis　has　been　in　increasing　demand　recently.　It　is　well　known　that　color　information　is　critical　to　the　automatic　and　visual　analysis　of　pathological　slides.　However,　the　color　variations　due　to　various　factors　not　only　have　negative　impact　on　pathologist＇s　diagnosis,　but　also　will　reduce　the　robustness　of　the　algorithms.　The　factors　that　cause　the　color　differences　are　not　only　in　the　process　of　making　the　slices,　but　also　in　the　process　of　digitization.　Different　strategies　have　been　proposed　to　alleviate　the　color　variations.　Most　of　such　techniques　rely　on　collecting　color　statistics　to　perform　color　matching　across　images　and　highly　dependent　on　a　reference　template　slide.　Since　the　pathological　slides　between　hospitals　are　usually　unpaired,　these　methods　do　not　yield　good　matching　results.　In　this　work,　we　propose　a　novel　network　that　we　refer　to　as　Transitive　Adversarial　Networks　(TAN)　to　transfer　the　color　information　among　slides　from　different　hospitals　or　centers.　It　is　not　necessary　for　an　expert　to　pick　a　representative　reference　slide　in　the　proposed　TAN　method.　We　compare　the　proposed　method　with　the　state-of-the-art　methods　quantitatively　and　qualitatively.　Compared　with　the　state-of-the-art　methods,　our　method　yields　an　improvement　of　0.87　dB　in　terms　of　PSNR,　demonstrating　the　effectiveness　of　the　proposed　TAN　method　in　stain　style　transfer.