# A Survey on the Recent Advances of Deep Community Detection
## 3 Deep Community Detection Strategies
### CNN and GNN Based Approaches
#### 3.2.2 GNN-Based Community Detection
The KNN graph is the input to the auto-encoder and the GNN. Each layer of the autoencoder is connected to the corresponding layer of GNN, so that the auto-encoder based representation can be integrated into the structure-aware representation. The overall time complexity of the proposed scheme is \(O(Nd^{2}d_{1}^{2}.....d_{L}^{2}+|E||\mathcal{E}|dd_{1}\ldots d_{L}+NK+N\log N)\), which is linear to the number of nodes. In this quantity, \(\mathcal{E}\) is the number of edges, and \(d\) is the number of samples. The Multi-View Attribute Graph Convolution Networks (MAGCN) [61] is based on two-pathway encoders that map graph embedding features and learn view-consistency information. The first pathway (Multi-view attribute graph convolution encoders) implements graph attention networks for multiple features, in order to reduce redundancies and learn the embedding features of the multiple view data. The second pathway (Consistent embedding encoders) further implements embedding encoders used to capture the geometry relationships and consistency of probability distribution among the information views. In this manner, the multi-view attributes are located in a clustered embedded space. Generally, the multi-view GNNs have the following limitations: (1) They cannot allocate learnable different weights to different nodes in neighborhood, and (2) The similarity is not explicitly considered for the consistency relationship among different views [62; 63]. The multi-view attribute graph convolution encoder equipped with attention mechanism for learning graph embedding from multi-view graph data provides the solution for learnable weight allocation to different nodes. In addition, the embedding encoders incorporate the geometric relationship and the probability distribution consistency among multi-view graph data, thus facilitating the clustering task. The Dual-Regularized Graph Convolutional Networks (DRGCN) [64] is the first work that studies the node-level class-imbalanced graph embedding problem using GNNs. The DRGCN strategy has been developed to handle multi-class imbalanced graphs. It imposes two regulation types to handle the representation learning (Class-Imbalanced Convolution Learning). Two more ideas are implemented: (a) a class-conditioned training process, which facilitates separating the labeled nodes, and (b) a process that makes the unlabeled nodes follow the same latent distribution to the labeled nodes. More specifically, the Class-Imbalanced Convolution Learning is a two-layer graph convolutional network, which is used for learning the node-level representation on the input graph. The two-order relationships between neighboring nodes are modeled in a sequential manner. The Class-Conditioned Adversarial Regularization is used to enhance the separation of different classes by imposing conditional training on all labeled nodes. Finally, the Latent Distribution Alignment Regularization is a a distribution alignment training of both labeled and unlabeled node representations under the assumption that the imbalanced classes in the unlabeled space will match the imbalanced classes in the labeled space.