# A Survey on the Recent Advances of Deep Community Detection
## 3 Deep Community Detection Strategies
### AE-Based Community Detection Strategies
Compared to strategies which are based on capturing the geometric structure of data using group distances [35], the CDMEC scheme manages to get low-dimensional representations in a more efficient way. Moreover, to reveal more comprehensive similarity relationships between the nodes of a network topology, the CDMEC introduced similarity matrices based to different functions. The Variational Graph Autoencoder (VGAE) [36; 37; 38] is an interesting method that captures the high-order features of a community structure. The VGAE has allowed models to achieve state-of-the-art performance for node clustering. In [36], a dual optimization procedure is introduced to guide the optimization process and encourage learning of the primary objective. The main objective here is to deal with the problem of extracting non-optimal communities, as a result of the loss optimization efforts through the stochastic gradient descent [37]. The proposed encoder is linearized and, in this way, the learning parameters are reduced. The time complexity of the proposed scheme is dictated by \(O(N^{2})\), where \(N\) is the number of network nodes. A number of approaches focus on reducing and sharing the trainable parameters, in order to improve the efficiency of deep learning models. Several approaches can be mentioned here: [PERSON] et al. [39] developed a method of trainable parameters reduction by sharing these trainable parameters. This method overcomes the timing and resource limitations and provides a good accuracy. In addition, [PERSON] et al. [40] proposed a method for reducing the trainable parameters by employing weight sharing over multiple scales in convolutional networks. In a more recent but different autoencoder based approach [41], the DACDRP (Deep Autoencoder-based Community Detection using the Reduction of trainable parameters by complex network Partitioning) strategy was introduced. The method was built based on partitioning complex networks into chunks, in order to reduce the necessary training parameters of the deep learning model. These chunks shared the same training parameters. The method ensures high connectivity among these chunks and introduces a new similarity constraint function that improves and the overall effectiveness. The DACDRP strategy was implemented on different levels of partitioning: DACDRP-L0, DACDRP-L1, and DACDRP-L2. An extra add-on of the proposed scheme is that it is fully parallelizable and thus it guarantees higher efficiency. Both CPU and GPU devices have been used for the implementation. Generally, for complex networks of size \(n\), the model time complexity is of order of \(n^{2}\), which is reduced by network splitting and parallelism. The Adaptive Graph Encoder (AGE) is another filter-related graph embedding model for community detection. [PERSON] et al. [42] proposed a two-part model for auto-encoder based community detection. The model consists of a Laplacian smoothing filter and an adaptive encoder. The smoothing filter is playing the role of a low-pass filter to de-noise the components of the feature matrix \(X\), which have very high frequencies. The smoothness of a graph signal \(\mathbf{x}\) is calculated by the Rayleigh quotient over the graph Laplacian \(L\) and \(\mathbf{x}\). This matrix is the input of the adaptive encoder, which is trained via a supervised method. Table 1 summarizes the auto-encoder based techniques presented in this section, in terms of the year of publication, the specific strategy being used for community, the overall time complexity whenever available, and the main experimental results found. Comments on the AE Based Community Detection Schemes  
After the study of the most recent AE-based community detection methods, we made the following observations: