Graph　neural　networks　(GNNs)　have　achieved　significant　success　in　numerous　graph-based　applications.　Unfortunately,　they　are　sensitive　to　adversarial　examples　generated　by　modifying　graphs　with　imperceptible　perturbations.　Therefore,　researchers　develop　attack　models　to　evaluate　the　robustness　of　GNNs　or　design　corresponding　defense　models.　However,　traditional　attack　models　can　hardly　determine　the　importance　of　perturbed　graph　structures,　where　the　selection　of　attack　targets　lacks　explainability.　Moreover,　these　attack　models　are　mainly　designed　for　certain　graph-learning　tasks.　In　this　study,　we　propose　a　two-level　adversarial　attack　framework　that　reconciles　task-and　feature-level　attacks　on　GNNs.　First,　instead　of　using　only　adversarial　examples,　we　introduce　a　dual-view　pipeline　with　two　task-level　optimization　objectives　that　consider　the　original　and　adversarial　examples　separately.　We　theoretically　demonstrate　that　this　simple　yet　powerful　loss　not　only　improves　attack　performance　but　also　exhibits　strong　explainability.　Second,　we　propose　a　feature-level　attack　framework　based　on　contrastive　learning　in　which　adversarial　attacks　are　applied　to　the　learned　features.　Our　theoretical　results　imply　that　contrastive　learning　between　original　and　adversarial　examples　can　destroy　the　representation　and　discriminative　abilities　of　GNNs.　Experimental　results　for　several　datasets　and　different　GNN　architectures　demonstrate　the　effectiveness　of　the　proposed　method.