  Community discovery is an important task for graph mining. Owing to the
nonstructure, the high dimensionality, and the sparsity of graph data, it is
not easy to obtain an appropriate community partition. In this paper, a deep
graph convolution neural network with non-negative matrix factorization
(DGCN-NMF) is proposed for community discovery. DGCN-NMF employs multiple graph
convolution layers to obtain the low dimensional embedding. In each layer, the
last output and the non-negative matrix factorization's results of the previous
outputs are inputted into the current layer. The community partition of the
inputted graph can be outputted by an end-to-end approach. The proposed
algorithm and the comparison algorithms are conducted on the experimental data
sets. The experimental results show that the proposed algorithm outperforms the
comparison algorithms on the experimental data sets. The experimental results
demonstrate that DGCN-NMF is an effective algorithm for community discovery.
