Due　to　the　powerful　capability　to　gather　the　information　of　neighborhood　nodes,　Graph　Convolutional　Network　(GCN)　has　become　a　widely　explored　hotspot　in　recent　years.　As　a　well-established　extension,　Graph　AutoEncoder　(GAE)　succeeds　in　mining　underlying　node　representations　via　evaluating　the　quality　of　adjacency　matrix　reconstruction　from　learned　features.　However,　limited　works　on　GAE　were　devoted　to　leveraging　both　semantic　and　topological　graphs,　and　they　only　indirectly　extracted　the　relationships　between　graphs　via　weights　shared　by　features.　To　better　capture　the　connections　between　nodes　from　these　two　types　of　graphs,　this　paper　proposes　a　graph　neural　network　dubbed　Dual　Low-Rank　Graph　AutoEncoder　(DLR-GAE),　which　takes　both　semantic　and　topological　homophily　into　consideration.　Differing　from　prior　works　that　share　common　weights　between　GCNs,　the　presented　DLR-GAE　conducts　sustained　exploration　of　low-rank　information　between　two　distinct　graphs,　and　reconstructs　adjacency　matrices　from　learned　latent　factors　and　embeddings.　In　order　to　obtain　valid　adjacency　matrices　that　meet　certain　conditions,　we　design　some　surrogates　and　projections　to　restrict　the　learned　factor　matrix.　We　compare　the　proposed　model　with　state-of-the-art　methods　on　several　datasets,　which　demonstrates　the　superior　accuracy　of　DLR-GAE　in　semi-supervised　classification.　Copyright　©　2023,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.