Graph　data　have　become　increasingly　important,　and　graph　node　clustering　has　emerged　as　a　fundamental　task　in　data　analysis.　In　recent　years,　graph　node　clustering　has　gradually　moved　from　traditional　shallow　methods　to　deep　neural　networks　due　to　the　powerful　representation　capabilities　of　deep　learning.　In　this　article,　we　review　some　representatives　of　the　latest　graph　node　clustering　methods,　which　are　classified　into　three　categories　depending　on　their　principles.　Extensive　experiments　are　conducted　on　real-world　graph　datasets　to　evaluate　the　performance　of　these　methods.　Four　mainstream　evaluation　performance　metrics　are　used,　including　clustering　accuracy,　normalized　mutual　information,　adjusted　rand　index,　and　F1-score.　Based　on　the　experimental　results,　several　potential　research　challenges　and　directions　in　the　field　of　deep　graph　node　clustering　are　pointed　out.　This　work　is　expected　to　facilitate　researchers　interested　in　this　field　to　provide　some　insights　and　further　promote　the　development　of　deep　graph　node　clustering.