Graphs　and　networks　are　very　common　data　structure　for　modelling　complex　systems　that　are　composed　of　a　number　of　nodes　and　topologies,　such　as　social　networks,　citation　networks,　biological　protein-protein　interactions　networks,　etc.　In　recent　years,　machine　learning　has　become　an　efficient　technique　to　obtain　representation　of　graph　for　downstream　graph　analysis　tasks,　including　node　classification,　link　prediction,　and　community　detection.　Different　with　traditional　graph　analytical　models,　the　representation　learning　on　graph　tries　to　learn　low　dimensional　embeddings　by　means　of　machine　learning　models　that　could　be　trained　in　supervised,　unsupervised　or　semi-supervised　manners.　Compared　with　traditional　approaches　that　directly　use　input　node　attributes,　these　embeddings　are　much　more　informative　and　helpful　for　graph　analysis.　There　are　a　number　of　developed　models　in　this　respect,　that　are　different　in　the　ways　of　measuring　similarity　of　vertexes　in　both　original　space　and　feature　space.　In　order　to　learn　more　efficient　node　representation　with　better　generalization　property,　we　propose　a　task-independent　graph　representation　model,　called　as　graph　deconvolutional　network　(GDN),　and　corresponding　unsupervised　learning　algorithm　in　this　paper.　Different　with　graph　convolution　network　(GCN)　from　the　scratch,　which　produces　embeddings　by　convolving　input　attribute　vectors　with　learned　filters,　the　embeddings　of　the　proposed　GDN　model　are　desired　to　be　convolved　with　filters　so　that　reconstruct　the　input　node　attribute　vectors　as　far　as　possible.　The　embeddings　and　filters　are　alternatively　optimized　in　the　learning　procedure.　The　correctness　of　the　proposed　GDN　model　is　verified　by　multiple　tasks　over　several　datasets.　The　experimental　results　show　that　the　GDN　model　outperforms　existing　alternatives　with　a　big　margin.　©　2020　Elsevier　Inc.