Graph　clustering　aims　to　divide　the　graph　nodes　into　distinct　clusters　in　an　unsupervised　manner,　which　usually　encodes　the　data　features　and　the　corresponding　graph　structure　into　a　latent　feature　space.　However,　we　observe　that　the　node　representations　learned　by　existing　graph　embedding　methods　are　always　of　poor　quality　and　may　contain　noise,　e.g.,　redundant　information　or　deficient　information,　which　significantly　affects　the　clustering　performance.　To　solve　this　problem,　we　propose　a　novel　Information　Correlation　Co-Supervision　based　Graph　Convolutional　Clustering　Model.　This　model　consists　of　a　hierarchical　deep　network　architecture,　i.e.　graph　auto-encoder　and　auto-encoder.　Specifically,　the　graph　convolutional　auto-encoder　(GCAE)　is　utilized　to　integrate　the　graph　structure　information　into　the　latent　feature　space,　and　the　auto-encoder　(AE)　is　proposed　to　learn　node　embedding.　In　order　to　make　the　node　representations　more　discriminate　and　more　robust　against　noise,　the　information　correlation　co-supervision　loss　is　designed　to　supervise　the　correlation　of　node　representations　learned　by　AE　and　GCAE.　Extensive　experiments　on　three　public　datasets　demonstrate　that　our　proposed　model　performs　better　than　the　multiple　state-of-the-art　deep　clustering　method.　©　2023　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.