# A Survey on the Recent Advances of Deep Community Detection
## 3 Deep Community Detection Strategies
### AE-Based Community Detection Strategies
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Year** & **Strategy** & **Complexity** & **Basic Experimental Results** \\ \hline
[26] & 2018 & Combines the models of modularity and normalized-cut via an auto-encoder & \(O(N)\) & Outperforms classic community detection schemes like the Louvian [43] or CNM [44] method or similar approaches that use only content or topological information. \\ \hline
[27] & 2019 & Two-phased approach; (1) the auto-encoder layered approach is used to initialize candidate seed nodes, (2) Refinement of the seed nodes and clusters in the last encoder layer. & \(O(n\log(n))\) & The selection of seed nodes reduces the number of iterations. The strategy scales up linearly produces better quality of clusters. \\ \hline
[32] & 2017 & Community Embedding framework that jointly solves community embedding, community detection and node embedding. & \(O(|V|+|E|)\) & Tested on real world datasets, it improves graph visualization and outperforms other schemes in application tasks, like community detection and node classification. \\ \hline
[33] & 2018 & Generative adversarial training process as a complementary regularizer & \(O(n)\) & Effectiveness on a variety of tasks, including network reconstruction, link prediction, and multi-label classification. \\ \hline
[34] & 2020 & Combines transfer learning and a stacked auto-encoder to produce a feature representation of complex networks in low dimension. & \(O(k\times L)\) & Experiments on artificial and real networks show that it is superior to the existing methods and has great potential in solving the community detection problems \\ \hline
[36] & 2020 & A dual optimization procedure, which aims at guiding the optimization process. & \(O(N^{2})\) & The performance gain is marginal as far as community detection is concerned, and the number of learnable parameters, is reduced; thus, faster convergence and training speed are achieved. \\ \hline
[41] & 2020 & Based on partitioning complex networks and on techniques for reduction and sharing of trainable parameters. & Not inferred & Improves training speed and efficiency, which increases in deeper level of partitioning. \\ \hline
[42] & 2020 & A Laplacian smoothing filter combined to an adaptive encoder. & Not inferred & Experiments on four datasets have shown that the scheme outperforms other graph embedding methods considerably on node clustering and link prediction tasks. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Auto-encoder based community detection strategies. 1. **The two kinds of information considered in community detection produce limited and inefficient models. Auto-encoders provide a solution:** The topology and node content information can be considered as objective functions to be combined in a linear form. However, this linearity is not certain in real world networks, and this makes the models limited. In addition, we can produce a network representation by combining the two types of information but, the optimization decision on the appropriate ration of the two types of information makes the model inefficient. Finally, the high-dimensional feature space of the input data can lead to the production of very complex neural network architectures and can increase the number of trainable parameters, which may reach millions. The auto-encoder is a good method to overcome the efficiency problems of linear optimization and to combine the two types of information. 2. **Auto-encoders are the dominant method for mapping data points into the lower dimensional spaces:** The disadvantage of the network high-dimensionality is the very large computational costs that the community detection methods suffer. Thus, a method of converting high-dimensional graphs into a space with fewer dimensions, in which the important information regarding the network structure and the node features are still maintained, is required.