# A Survey on the Recent Advances of Deep Community Detection
## 3 Deep Community Detection Strategies
### CNN and GNN Based Approaches
#### 3.2.1 CNN-Based Community Detection  
The CNNs are an attractive solution for community detection because they combine the convolution application to pooling. A pooling layer is a layer added after the convolutional layer and its objective is to reduce the data dimensionality. A quite recent idea for CNN based models is the introduction of Markov Random Fields (MRF). MRF have been used in other applications like image processing. A first attempt was made in [47], where a network MRF (NetMRF) model was presented, to encode the modular properties of an irregular network in the energy function to describe communities. The MRF was converted to a convolutional layer, which was incorporated into CNN. The drawback of NetMRF is that it does not consider information on nodes and requires a substantial amount of computation for learning the model. On the contrary, the MRFaSGCN [48] exploits both network topology and node semantic information in an end-to-end deep network architecture. The approach is based on adding an MRF model as a new convolutional layer: the MRF model and its inference is first transformed to a convolutional layer and then added as the third (and last) layer of the basic two-layer convolutional network architecture. The third convolutional layer refines the result by performing the MRF. The final output is obtained by subtracting the refined result from first two layers. The total computational time complexity for all the layers is \(O(emhk^{2})\), where \(e,m\), and \(k\) are the numbers of edges, attributes, and communities, respectively, while \(h\) is the number of hidden units of the first layer. The above methods are semi-supervised. [PERSON] et al. [49] extended the idea of combining Markov Random Fields (MRF) with CCN [48] and developed an approach for unsupervised community detection, by adding an auto-encoder. The MRFaSGCN is cast as an encoder and the node community membership is found in the hidden layer of the encoder. A dual decoder is used to separately reconstruct the network structures and node attributes in an unsupervised fashion to obtain reliable community detection.  
Two new approaches can be found in [50; 51]. [PERSON]'s approach [50] is based on deep learning approaches and on the topological properties of social networks. Specifically, it leverages a particular CNN that has been modified so that it can deal with large dimensions and high sparsity of adjacency matrices. Two convolutional steps are used, and each one is composed of a convolution and a max-pooling layer for topology learning of the input network, plus a full connection layer. Because the pooling layer organizes the input in some defined areas and stores an aggregate value for each area, the computational time complexity is reduced. In [51], a scheme based on learning ground-truth communities for community detection over large networks is introduced. The proposed scheme uses an edge-to-image (E2I) model that can map the edge network structure to an image structure. Two type of edges are defined and classified: edges of the same community and edges that lie in different communities. This classification facilitates breadth search over the network to get localized community images.  
Generally, the CNN and its variants have been widely adopted in network embedding [52; 53; 54; 55]. For example, [PERSON] et al. [52] re-formatted the complex network topology adjacent matrix into an image and designed a CNN model to extract and classify relevant features. [PERSON] et al. used MeshCNN to analyze 3D shapes directly, in order to leverage their intrinsic connections. Inspired by these works, [PERSON] et al. [24] introduced a CNN + AE (Convolutional Neuran Network + Autoencoder) approach to implement unsupervised community detection. The introduction of an autoencoder guarantees of a more efficient extraction of the spatial features of social networks and improves the community detection. In addition, the combined model can become the basis for dynamic community detection.