In this paper, we propose a novel method, Complementary Multi-View Neighborhood Differentiation (CMV-ND), to address the deep graph clustering (DGC) problem in large-scale, missing-attribute real-world attribute graphs. CMV-ND preprocesses the structural information of the graph into multiple views that are complete and non-redundant. This is implemented by fully expanding the node neighborhood over various hop distances through recursive neighbor search, and removing redundant nodes between different hop representations through neighbor differentiation strategy. Finally, $K+1$ complementary views are constructed from $K$ differential hop representations and the features of target nodes, and conventional multi-view clustering or DGC methods are applied. Experimental results on six popular graph datasets show that CMV-ND significantly improves the performance of various methods.