  \textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork (\textbf{GCN}) is
widely used in graph data learning tasks such as recommendation. However, when
facing a large graph, the graph convolution is very computationally expensive
thus is simplified in all existing GCNs, yet is seriously impaired due to the
oversimplification. To address this gap, we leverage the \textit{original graph
convolution} in GCN and propose a \textbf{L}ow-pass \textbf{C}ollaborative
\textbf{F}ilter (\textbf{LCF}) to make it applicable to the large graph. LCF is
designed to remove the noise caused by exposure and quantization in the
observed data, and it also reduces the complexity of graph convolution in an
unscathed way. Experiments show that LCF improves the effectiveness and
efficiency of graph convolution and our GCN outperforms existing GCNs
significantly. Codes are available on \url{https://github.com/Wenhui-Yu/LCFN}.
