Graph　Neural　Networks　(GNNs)　have　exhibited　remarkable　capabilities　for　dealing　with　graph-structured　data.　However,　recent　studies　have　revealed　their　fragility　to　adversarial　attacks,　where　imperceptible　perturbations　to　the　graph　structure　can　easily　mislead　predictions.　To　enhance　adversarial　robustness,　some　methods　attempt　to　learn　robust　representation　through　improving　GNN　architectures.　Subsequently,　another　approach　suggests　that　these　GNNs　might　taint　feature　information　and　have　poor　classifier　performance,　leading　to　the　introduction　of　Graph　Contrastive　Learning　(GCL)　methods　to　build　a　refining-classifying　pipeline.　However,　existing　methods　focus　on　global-local　contrastive　strategies,　which　fails　to　address　the　robustness　issues　inherent　in　the　contexts　of　adversarial　robustness.　To　address　these　challenges,　we　propose　a　novel　paradigm　named　GRANCE　to　enhance　the　robustness　of　learned　representations　by　shifting　the　focus　to　local　neighborhoods.　Specifically,　a　dual　neighborhood　contrastive　learning　strategy　is　designed　to　extract　local　topological　and　semantic　information.　Paired　with　a　neighbor　estimator,　the　strategy　can　learn　robust　representations　that　are　resilient　to　adversarial　edges.　Additionally,　we　also　provide　an　improved　GNN　as　classifier.　Theoretical　analyses　provide　a　stricter　lower　bound　of　mutual　information,　ensuring　the　convergence　of　GRANCE.　Extensive　experiments　validate　the　effectiveness　of　GRANCE　compared　to　state-of-the-art　baselines　against　various　adversarial　attacks.　Copyright　©　2025,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.