Multi-view　clustering　(MVC)　aims　to　extract　consensus　information　from　multi-source　data　and　has　developed　rapidly.　While　generative　model-based　methods　perform　well　by　leveraging　predefined　priors,　they　often　overlook　inter-instance　relationships,　which　are　essential　for　high-quality　clustering.　To　address　this　issue,　we　propose　Graph　Variational　Multi-view　Clustering　(GVMVC),　which　integrates　graph　information　into　the　generative　process.　Specifically,　we　treat　both　the　original　multi-view　features　and　graph　information　from　each　view　as　observed　data,　guiding　the　learning　of　latent　representations.　The　key　principles　of　this　approach　are:　1)　enhancing　discriminative　feature　learning　through　graph　integration,　and　2)　ensuring　consistent　multi-view　learning　via　graph-based　constraints.　Extensive　experiments　show　that　GVMVC　outperforms　state-of-the-art　methods　across　various　datasets　and　metrics.　Code　is　available　at　https://github.com/WenB777/GVMVC.git.　　©　1991-2012　IEEE.