Although　Graph　Neural　Networks　(GNNs)　have　exhibited　the　powerful　ability　to　gather　graph-structured　information　from　neighborhood　nodes　via　various　message-passing　mechanisms,　the　performance　of　GNNs　is　limited　by　poor　generalization　and　fragile　robustness　caused　by　noisy　and　redundant　graph　data.　As　a　prominent　solution,　Graph　Augmentation　Learning　(GAL)　has　recently　received　increasing　attention　in　the　literature.　Among　the　existing　GAL　approaches,　edge-dropping　methods　that　randomly　remove　edges　from　a　graph　during　training　are　effective　techniques　to　improve　the　robustness　of　GNNs.　However,　randomly　dropping　edges　often　results　in　bypassing　critical　edges.　Consequently,　the　effectiveness　of　message　passing　is　weakened.　In　this　paper,　we　propose　a　novel　adversarial　edgedropping　method　(ADEdgeDrop)　that　leverages　an　adversarial　edge　predictor　guiding　the　removal　of　edges,　which　can　be　flexibly　incorporated　into　diverse　GNN　backbones.　Employing　an　adversarial　training　framework,　the　edge　predictor　utilizes　the　line　graph　transformed　from　the　original　graph　to　estimate　the　edges　to　be　dropped,　which　improves　the　interpretability　of　the　edge-dropping　method.　The　proposed　ADEdgeDrop　is　optimized　alternately　by　stochastic　gradient　descent　and　projected　gradient　descent.　Comprehensive　experiments　on　eight　graph　benchmark　datasets　demonstrate　that　the　proposed　ADEdgeDrop　outperforms　state-of-the-art　baselines　across　various　GNN　backbones,　demonstrating　improved　generalization　and　robustness.　©　1989-2012　IEEE.