# A Survey on the Recent Advances of Deep Community Detection
## 3 Deep Community Detection Strategies
### GAN Based Approaches
Furthermore, the fake attribute network is not based only on the Gaussian distribution, as in GANs, but its generation is based on combining the embedding results of real attribute networks with the samples generated from Gaussian distribution. The Discriminator module is input with the real and fake topology and node features and embeddings and jointly distinguishes the real and fake combinations. The fake inputs are produced by the generator, based on the samples taken from a Gaussian distribution. Table 3 summarizes the most important features of the GAN-based strategies. Comments on the GAN-Based Community Detection Schemes  
After the study of the most recent GAN-based community detection schemes, the following observations can be made:  
1. **Regularizations:** Deep learning strategies (AE- or GNN- based) focus on preserving the structure relationship and generally ignore the latent data distribution of the representation. Using GANs, the new data generated have the same distribution as real data, and this is a powerful tool for analyzing network data. For example, in [67], some form of regularization is introduced to the latent codes, thereby forcing them to follow some prior data distribution. 2. **Dense Overlapping Issues:** In real-world datasets, vertices can simultaneously belong to numerous communities and clustering algorithms cannot handle such dense overlapping. A weakness in performing overlapping community detection and network embedding simultaneously has been reported (see also our comments in the presentation of AE and CNN/GNN-based schemes). An idea is to combine the effective GANS with affiliation graph models (AGM), which can model densely overlapping community structures. Thus, the performance of GANs and the direct vertex-community membership representation of AGMs join forces to solve the dense overlapping issues [68]. In addition, the scheme presented in [69] is working on overlaps. 3. **Undistinguished real and not real embedding combinations:** Most of the GANs compare the generated fake data, which are produced based on Gaussian distribution sampling to the real data. In adversarial learning approaches, the Gaussian distribu
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Year** & **Strategy** & **Complexity** & **Basic Experimental Results** \\ \hline \multirow{4}{*}{[67]} & \multirow{4}{*}{2020} & Two combined modules: a graph convolutional auto-encoder, which reconstructs a given graph from a latent representation and an adversarial network which is trained to discriminate if a sample is generated from the embedding. & \multirow{4}{*}{\(O(|E|md)\)} & Good average node clustering performance Accuracy (ACC), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI) and good link prediction with different prior distributions compared to a large number of competitive schemes. & \multirow{4}{*}{\(O(|E|md)\)} & Good average node clustering performance Accuracy (ACC), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI) and good link prediction with different prior distributions compared to a large number of competitive schemes. & \multirow{4}{*}{\(O(|E|md)\)} & \multirow{4}{*}{\ \begin{tabular}{} \end{tabular}  } \\ \cline{1-1}  & & Combines AGM and GAN in a unified framework, which achieves good GAN performance and direct vertex-community membership representation in AGM. The Graph AGM generates the most likely motifs with graph structure awareness. & \multirow{4}{*}{Not inferred bi-matching F1 and Jaccard scores.} & \multirow{4}{*}{\(O(V(V+E))\)} & \multirow{4}{*}{ \begin{tabular}{} \end{tabular}  } \\ \cline{1-  
tions cannot be rectified with real data so the results are not really beneficial for the network embeddings. Approaches like JANE [71] can be used to separate real and fake combinations of embeddings, topology information, and node features. Because of the combined topology information and node features, the Gaussian distribution is capable of capturing the semantic variations in latent space.