Graph　neural　networks　(GNNs)　exhibit　a　robust　capability　for　representation　learning　on　graphs　with　complex　structures,　demonstrating　superior　performance　across　various　applications.　Most　existing　GNNs　utilize　graph　convolution　operations　that　integrate　both　attribute　and　structural　information　through　coupled　way.　And　these　GNNs,　from　an　optimization　perspective,　seek　to　learn　a　consensus　and　compromised　embedding　representation　that　balances　attribute　and　graph　information,　selectively　exploring　and　retaining　valid　information　in　essence.　To　obtain　a　more　comprehensive　embedding　representation,　a　novel　GNN　framework,　dubbed　Decoupled　Graph　Neural　Networks　(DGNN),　is　introduced.　DGNN　separately　explores　distinctive　embedding　representations　from　the　attribute　and　graph　spaces　by　decoupled　terms.　Considering　that　the　semantic　graph,　derived　from　attribute　feature　space,　contains　different　node　connection　information　and　provides　enhancement　for　the　topological　graph,　both　topological　and　semantic　graphs　are　integrated　by　DGNN　for　powerful　embedding　representation　learning.　Further,　structural　consistency　between　the　attribute　embedding　and　the　graph　embedding　is　promoted　to　effectively　eliminate　redundant　information　and　establish　soft　connection.　This　process　involves　facilitating　factor　sharing　for　adjacency　matrices　reconstruction,　which　aims　at　exploring　consensus　and　high-level　correlations.　Finally,　a　more　powerful　and　comprehensive　representation　is　achieved　through　the　concatenation　of　these　embeddings.　Experimental　results　conducted　on　several　graph　benchmark　datasets　demonstrate　its　superiority　in　node　classification　tasks.　　©　2015　IEEE.