In　recent　years,　reconstructing　features　and　learning　node　representations　by　graph　autoencoders　(GAE)　have　attracted　much　attention　in　deep　graph　node　clustering.　However,　existing　works　often　overemphasize　structural　information　and　overlook　the　impact　of　real-world　prevalent　noise　on　feature　learning　and　clustering　with　graph　data,　which　may　be　detrimental　to　robust　training.　To　address　these　issues,　the　utilization　of　a　masking　strategy　that　specifically　focuses　on　feature　reconstruction　may　mitigate　these　limitations.　In　this　article,　we　propose　a　graph　node　clustering　generative　method　named　deep　masked　graph　node　clustering　(DMGNC),　which　leverages　a　masked　autoencoder　to　effectively　reconstruct　node　features,　enabling　the　discovery　of　latent　information　crucial　for　accurate　node　clustering.　Additionally,　a　clustering　self-optimization　module　is　designed　to　guide　the　iterative　update　of　our　end-to-end　clustering　framework.　Further,　we　extend　the　masked　graph　autoencoder　(MGA)　and　develop　a　contrastive　method　called　deep　masked　graph　node　contrastive　clustering　(DMGNCC),　which　applies　the　MGA　to　graph　node　contrastive　learning　at　both　the　node　level　and　the　class　level　in　a　united　model.　Extensive　experimental　results　on　real-world　graph　benchmark　datasets　demonstrate　the　effectiveness　and　superiority　of　the　proposed　method.