Graph　Neural　Networks　(GNNs)　are　powerful　tools　for　graph　representation　learning,　but　they　face　challenges　when　applied　to　large-scale　graphs　due　to　substantial　computational　costs　and　memory　requirements.　To　address　scalability　limitations,　various　methods　have　been　proposed,　including　samplingbased　and　decoupling-based　methods.　However,　these　methods　have　their　limitations:　sampling-based　methods　inevitably　discard　some　link　information　during　the　sampling　process,　while　decoupling-based　methods　require　alterations　to　the　model's　structure,　reducing　their　adaptability　to　various　GNNs.　This　paper　proposes　a　novel　graph　pooling　method,　Graph　Partial　Pooling　(GPPool),　for　scaling　GNNs　to　large-scale　graphs.　GPPool　is　a　versatile　and　straightforward　technique　that　enhances　training　efficiency　while　simultaneously　reducing　memory　requirements.　GPPool　constructs　small-scale　pooled　graphs　by　pooling　partial　nodes　into　supernodes.　Each　pooled　graph　consists　of　supernodes　and　unpooled　nodes,　preserving　valuable　local　and　global　information.　Training　GNNs　on　these　graphs　reduces　memory　demands　and　enhances　their　performance.　Additionally,　this　paper　provides　a　theoretical　analysis　of　training　GNNs　using　GPPool-constructed　graphs　from　a　graph　diffusion　perspective.　It　shows　that　a　GNN　can　be　transformed　from　a　large-scale　graph　into　pooled　graphs　with　minimal　approximation　error.　A　series　of　experiments　on　datasets　of　varying　scales　demonstrates　the　effectiveness　of　GPPool.　IEEE