Data　augmentation　has　been　successfully　utilized　to　refine　the　generalization　capability　and　performance　of　learning　algorithms　in　image　and　text　analysis.　With　the　rising　focus　on　graph　neural　networks,　an　increasing　number　of　researchers　are　employing　various　data　augmentation　approaches　to　improve　graph　learning　techniques.　Although　significant　improvements　have　been　made,　most　of　them　are　implemented　by　manipulating　nodes　or　edges　to　generate　modified　graphs　as　augmented　views,　which　might　lose　the　information　hidden　in　the　input　data.　To　address　this　issue,　we　propose　a　simple　but　effective　data　augmentation　framework　termed　multi-relation　augmentation　designed　for　existing　graph　neural　networks.　Different　from　prior　works,　the　designed　model　utilizes　various　methods　to　simulate　multiple　adjacency　relationships　(multi-relation)　among　nodes　as　augmented　views　instead　of　manipulating　the　original　graph.　The　proposed　augmentation　framework　can　be　formulated　as　three　sub-modules,　each　offering　distinct　advantages:　1)　The　encoder　module　and　projection　module　form　a　shared　contrastive　learning　framework　for　both　the　original　graph　and　all　augmented　views.　Due　to　the　shared　mechanism,　the　proposed　method　can　be　simply　applied　to　various　graph　learning　models.　2)　The　designed　task-specific　module　flexibly　extends　the　proposed　framework　for　various　machine　learning　tasks.　Experimental　results　on　several　databases　show　that　the　introduced　augmentation　framework　improves　the　performance　of　existing　graph　neural　networks　on　both　semi-supervised　node　classification　and　unsupervised　clustering　tasks.　It　demonstrates　that　multiple　relations　mechanism　is　efficient　for　graph-based　augmentation.　IEEE