  A community reveals the features and connections of its members that are
different from those in other communities in a network. Detecting communities
is of great significance in network analysis. Despite the classical spectral
clustering and statistical inference methods, we notice a significant
development of deep learning techniques for community detection in recent years
with their advantages in handling high dimensional network data. Hence, a
comprehensive overview of community detection's latest progress through deep
learning is timely to academics and practitioners. This survey devises and
proposes a new taxonomy covering different state-of-the-art methods, including
deep learning-based models upon deep neural networks, deep nonnegative matrix
factorization and deep sparse filtering. The main category, i.e., deep neural
networks, is further divided into convolutional networks, graph attention
networks, generative adversarial networks and autoencoders. The survey also
summarizes the popular benchmark data sets, evaluation metrics, and open-source
implementations to address experimentation settings. We then discuss the
practical applications of community detection in various domains and point to
implementation scenarios. Finally, we outline future directions by suggesting
challenging topics in this fast-growing deep learning field.
