Graph　convolutional　networks　have　significant　advantages　in　dealing　with　graph-structured　data,　but　most　existing　methods　usually　potentially　assume　that　nodes　belonging　to　the　same　class　in　a　graph　tend　to　form　edges,　yet　inter-class　edges　exist　in　many　real-world　graph-structured　data.　Due　to　the　propagation　mechanism　of　graph　convolutional　networks,　it　is　challenging　to　prevent　the　interference　aggregation　from　nodes　of　different　classes,　which　may　result　in　the　incorporation　of　noise　and　irrelevant　data　in　the　outcome,　ultimately　decreasing　the　performance　of　the　model.　In　this　paper,　we　propose　a　new　framework　to　address　this　issue　on　heterophilous　graph-structured　data.　The　proposed　method　comprises　two　main　components.　On　one　hand,　the　homophily　of　the　graph-structured　data　is　modeled　so　that　the　method　can　adaptively　adjust　the　information　propagation　process　according　to　the　homophily　of　the　edges,　and　mitigate　the　influence　of　inter-class　information.　On　the　other　hand,　the　implicit　node　interaction　is　captured　through　the　learned　feature　space,　which　is　then　fused　with　the　original　interaction　to　aggregate　sufficient　intra-class　knowledge.　Extensive　experiments　on　real-world　datasets　demonstrate　the　superiority　of　the　proposed　method　against　current　state-of-the-art　approaches.　©　2024　Elsevier　B.V.