  Community detection is a fundamental and important issue in network science,
but there are only a few community detection algorithms based on graph neural
networks, among which unsupervised algorithms are almost blank. By fusing the
high-order modularity information with network features, this paper proposes a
Variational Graph AutoEncoder Reconstruction based community detection VGAER
for the first time, and gives its non-probabilistic version. They do not need
any prior information. We have carefully designed corresponding input features,
decoder, and downstream tasks based on the community detection task and these
designs are concise, natural, and perform well (NMI values under our design are
improved by 59.1% - 565.9%). Based on a series of experiments with wide range
of datasets and advanced methods, VGAER has achieved superior performance and
shows strong competitiveness and potential with a simpler design. Finally, we
report the results of algorithm convergence analysis and t-SNE visualization,
which clearly depicted the stable performance and powerful network modularity
ability of VGAER. Our codes are available at https://github.com/qcydm/VGAER.
