As　an　important　technology　for　recommendation　system,　attributed　graph　clustering　has　received　extensive　attention　recently.　Attributed　graph　clustering　methods　based　on　graph　neural　networks　are　the　mainstream　methods.　However,　their　assumption　that　the　attributes　of　adjacent　nodes　are　similar　is　often　not　satisfied　in　the　real　world,　which　influences　the　clustering　performance.　To　this　end,　this　paper　proposes　an　attributed　graph　subspace　clustering　algorithm　with　residual　compensation　guided　by　adaptive　dual　manifold　regularization　(ADMRGC).　On　the　basis　of　the　low　rank　representation　subspace　clustering　(LRR)　model,　ADMRGC　introduces　attributed　manifold　regularization,　topological　manifold　regularization　and　residual　compensation,　which　make　ADMRGC　possible　to　utilize　attributed　similarity　and　topological　similarity　at　the　same　time,　thus　solving　the　problem　that　traditional　subspace　clustering　only　considers　attributed　information.　In　addition,　ADMRGC　balances　the　contribution　of　node　attributes　and　topology　by　adaptively　weighting　the　dual　manifold　regularization,　and　uses　the　residual　representation　matrix　to　describe　the　difference　between　node　attributed　similarity　and　topological　neighbor　relationship.　Therefore,　ADMRGC　can　avoid　the　limitation　of　graph　neural　network　models　assuming　that　the　attributes　of　adjacent　nodes　are　similar.　Experimental　results　on　7　public　graph　datasets　show　that　ADMRGC　can　achieve　the　best　clustering　performance　on　both　high-homophily　and　low-homophily　datasets.　Especially,　for　datasets　with　low　homophily,　ADMRGC　as　a　shallow　model　can　also　achieve　better　clustering　performance　than　state-of-the-art　deep　neural　network　models.　©　2024