Node　classification　tasks　have　seen　considerable　progress　with　the　use　of　Graph　Neural　Networks　(GNNs).　However,　they　cannot　work　well　on　imbalanced　node　classification　and　tend　to　prioritize　the　majority　classes　with　more　labeled　instances　while　overlooking　the　minority　classes　with　fewer　labeled　instances.　Existing　solutions　focus　on　generating　new　nodes　to　augment　the　training　set,　which　may　disrupt　the　original　topological　structure　of　the　graph,　so　GNNs　may　not　achieve　optimal　classification　results.　To　address　this　issue,　we　introduce　GraphMMC,　a　reliable　and　flexible　strategy　to　generate　pseudo-labels　that　can　be　easily　integrated　with　various　GNNs,　which　will　augment　the　imbalance　training　set　to　a　class-balanced　set　without　generating　new　nodes.　We　use　the　similarity　between　the　unlabeled　nodes　and　the　minority　classes　to　correction　the　low-confidence　pseudo-labels　generated　by　GNNs　to　obtain　reliable　pseudo-labels.　Our　experiments　demonstrate　that　the　proposed　method　outperforms　state-of-the-art　baselines　on　several　class-imbalanced　datasets.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.