As　a　powerful　model　for　deep　learning　on　graph-structured　data,　the　scalability　limitation　of　Graph　Neural　Networks　(GNNs)　are　receiving　increasing　attention.　To　tackle　this　limitation,　two　categories　of　scalable　GNNs　have　been　proposed:　sampling-based　and　model　simplification　methods.　However,　sampling-based　methods　suffer　from　high　communication　costs　and　poor　performance　due　to　the　sampling　process.　Conversely,　existing　model　simplification　methods　only　rely　on　parameter-free　feature　propagation,　disregarding　its　spectral　properties.　Consequently,　these　methods　can　only　capture　low-frequency　information　while　disregarding　valuable　middle-　and　high-frequency　information.　This　paper　proposes　Automatic　Filtering　Graph　Neural　Networks　(AutoFGNN),　a　framework　that　can　extract　all　frequency　information　from　large-scale　graphs.　AutoFGNN　employs　parameter-free　low-,　middle-,　and　high-pass　filters,　which　extract　the　corresponding　information　for　all　nodes　without　introducing　parameters.　To　merge　the　extracted　features,　a　trainable　transformer-based　information　fusion　module　is　utilized,　enabling　AutoFGNN　to　be　trained　in　a　mini-batch　manner　and　ensuring　scalability　for　large-scale　graphs.　Experimental　results　show　that　AutoFGNN　outperforms　existing　methods　on　various　scale　graphs.　©　2024　IEEE.