Contrastive　learning　has　been　widely　used　in　graph　representation　learning,　which　extracts　node　or　graph　representations　by　contrasting　positive　and　negative　node　pairs.　It　requires　node　representations　(embeddings)　to　reflect　their　correlations　in　topology,　increasing　the　similarities　between　an　anchor　node　and　its　positive　nodes,　or　reducing　the　similarities　with　its　negative　nodes　in　embedding　space.　However,　most　existing　contrastive　models　measure　similarities　through　a　fixed　metric　that　equally　scores　all　sample　pairs　in　a　specific　feature　space,　but　ignores　the　varieties　of　node　attributes　and　network　topologies.　Moreover,　these　fixed　metrics　are　always　defined　explicitly　and　manually,　which　makes　them　unsuitable　for　applying　to　all　graphs　and　networks.　To　solve　these　problems,　we　propose　a　novel　graph　representation　learning　model　with　an　adaptive　metric,　called　GRAM,　which　produces　appropriate　similarity　scores　of　node　pairs　according　to　the　different　significance　of　each　dimension　in　their　embedding　vectors　and　adaptive　metrics　based　on　data　distribution.　With　these　scores,　it　is　better　to　train　a　graph　encoder　and　obtain　representative　embeddings.　Experimental　results　show　that　GRAM　has　strong　competitiveness　in　multiple　tasks.