It　has　been　proven　that　Graph　Neural　Networks　focus　more　on　the　majority　class　instances　and　ignore　minority　class　instances　when　the　class　distribution　is　imbalanced.　To　address　the　class　imbalance　problems　on　graphs,　most　of　the　existing　approaches　rely　on　the　availability　of　minority　nodes　in　the　training　set,　which　may　be　scarce　in　extremely　imbalanced　situations　and　lead　to　overfitting.　To　tackle　this　issue,　this　paper　proposes　a　novel　oversampling-based　Neighbor　imbalanced-aware　Graph　Neural　Networks,　abbreviated　as　Nia-GNNs.　Specifically,　we　propose　a　novel　interpolation　method　that　selects　interpolated　minority　nodes　from　the　entire　dataset　according　to　their　predicted　labels　and　similarity.　Meanwhile,　a　class-wise　interpolation　ratio　is　applied　to　prevent　the　generation　of　out-of-domain　nodes.　Additionally,　the　generated　minority　nodes　are　inserted　into　the　neighbor　of　minority　nodes　according　to　their　neighbor　distribution　to　balance　the　graph　both　neighborly　and　globally.　Numerous　experiments　on　different　imbalanced　datasets　demonstrate　the　superiority　of　our　method　in　classifying　imbalanced　nodes.