Graph　Neural　Networks　(GNNs)　have　expressed　remarkable　capability　in　processing　graph-structured　data.　Recent　studies　have　found　that　most　GNNs　rely　on　the　homophily　assumption　of　graphs,　leading　to　unsatisfactory　performance　on　heterophilous　graphs.　While　certain　methods　have　been　developed　to　address　heterophilous　links,　they　lack　more　precise　estimation　of　high-order　relationships　between　nodes.　This　could　result　in　the　aggregation　of　excessive　interference　information　during　message　propagation,　thus　degrading　the　representation　ability　of　learned　features.　In　this　work,　we　propose　a　Disparity-induced　Structural　Refinement　(DSR)　method　that　enables　adaptive　and　selective　message　propagation　in　GNN,　to　enhance　representation　learning　in　heterophilous　graphs.　We　theoretically　analyze　the　necessity　of　structural　refinement　during　message　passing　grounded　in　the　derivation　of　error　bound　for　node　classification.　To　this　end,　we　design　a　disparity　score　that　combines　both　features　and　structural　information　at　the　node　level,　reflecting　the　connectivity　degree　of　hopping　neighbor　nodes.　Based　on　the　disparity　score,　we　can　adjust　the　aggregation　of　neighbor　nodes,　thereby　mitigating　the　impact　of　irrelevant　information　during　message　passing.　Experimental　results　demonstrate　that　our　method　achieves　competitive　performance,　mostly　outperforming　advanced　methods　on　both　homophilous　and　heterophilous　datasets.　©　2025　Copyright　held　by　the　owner/author(s).