# A Survey on the Recent Advances of Deep Community Detection
## 3 Deep Community Detection Strategies
### GAN Based Approaches  
Typically, the generative adversarial networks (GANs) are employing two competing deep neural networks: a generator and a discriminator. The latter discriminates if an input sample comes from the prior data distribution or from the generator and the former is trained to generate the samples in such a way that the discriminator is convinced that the samples come from a prior data distribution [67]. The result of this scheme is high training precision. Recently, the GANs have been used to community detection problems and have presented high efficiency. In [67], the authors presented an adversarially regularized framework for graph embedding. The proposed strategy used the graph convolutional network as an encoder, allowing the embedding of the topology and node information into a vector representation. This representation is used to reconstruct the input graph. Two variants of the adversarial models were derived, the adversarially regularized graph auto-encoder (ARGA), as well as the adversarially regularized variational graph auto-encoder (ARVGA), to efficiently learn the graph embedding. The architecture of the ARGA consists of (i) a graph convolutional auto-encoder, which reconstructs a given graph from a latent representation. The latent representation has been produced by the encoder that is input with the graph structure (adjacency matrix) and the node content matrix, and (ii) an adversarial network which is trained to discriminate if a sample is generated from the embedding or from a prior distribution (regularization). The ARVGA architecture is the same as ARGA, but it employs a variational graph autoencoder instead of a graph convolutional auto-encoder (in part i). The convolution time complexity is \(O(|E|md)\), where \(E\) represents an encoding of edges between the nodes, \(d\) is the dimension of the latent variable, and \(m\) is the sample entities from the latent matrix or prior distribution. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Year** & **Strategy** & **Complexity** & **Basic Experimental Results** \\ \hline
[48] & 2019 & **CNN-Based:** The MRF model and its inference are transformed to a convolutional layer, and it is added as the third (and last) layer of the basic two-layer convolutional network architecture. & \(O(emtk^{2})\) & Superior performance over state-of-the-art methods in terms of accuracy, normalized mutual information (NMI) and runtime— in addition, high scalability on several large benchmark problems. & Superior performance over state-of-the-art methods in terms of accuracy, normalized mutual information (NMI) and runtime— in addition, high scalability on several large benchmark problems. & Superior performance over state-of-the-art methods in terms of accuracy, normalized mutual information (NMI) and runtime— in addition, high scalability on several large benchmark problems. & Superior performance over state-of-the-art methods in terms of accuracy, normalized mutual information (NMI) and runtime— in addition, high scalability on several large benchmark problems. & Improved performance in terms of accuracy, normalized mutual information (NMI) and runtime—in addition, high scalability on several large benchmark problems. & \\ \hline
[50] & 2019 & **CNN-Based:** Uses two convolutional steps, each composed by a convolution and a max-pooling layer for topology learning of the input network, plus a full connection layer to refine the results. & Improved efficiency of execution time (in terms of the density variation of sparse matrix), especially for larger node numbers and improved accuracy of predictions. & Improved efficiency of execution time (in terms of the density variation of sparse matrix), especially for larger node numbers and improved accuracy of  
CommunityGAN [68] is a community detection scheme that jointly solves overlapping community detection and graph representation learning.